Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a fault detection system for an electric system of a motorcycle.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the motorcycle electrical system fault detection system comprises a fault signal identification module, a fault mode analysis module, a genetic algorithm optimization module, a causal relationship modeling module, a multi-layer network analysis module, an association rule mining module, an extreme condition analysis module and a dynamic threshold adjustment module;
The fault signal identification module is used for carrying out signal processing by adopting a Fourier transform algorithm based on sensor data of an electric system of the motorcycle, extracting signal characteristics, removing noise by a filter, and identifying and establishing an abnormal signal set;
The fault mode analysis module classifies and reason-analyzes differentiated fault signals based on an abnormal signal set by adopting a fault tree analysis and statistical analysis method to generate classified fault modes;
The genetic algorithm optimization module optimizes the fault diagnosis strategy by utilizing a genetic algorithm based on the classified fault modes, and generates an optimized diagnosis strategy through iterative adjustment of a diagnosis flow and automatic optimization of parameters;
The causal relation modeling module is used for modeling and analyzing causal relation among components in the electrical system by adopting a Bayesian network based on an optimized diagnosis strategy, revealing potential reasons and propagation paths of faults and generating a causal relation model;
the multi-layer network analysis module is used for analyzing a multi-layer structure of the electrical system based on a causal relation model by utilizing graph theory, revealing interaction and dependency between different layers and generating a network interaction graph;
The association rule mining module analyzes interaction data among components by adopting an Apriori algorithm based on a network interaction graph, and identifies potential association modes and fault trigger factors to generate an association rule set;
The extreme condition analysis module simulates the performance of an electric system under extreme working conditions by adopting a Monte Carlo simulation method based on an association rule set, recognizes the response of the system under extreme environments, and generates extreme working condition reaction analysis;
The dynamic threshold adjustment module dynamically adjusts the fault detection threshold according to real-time monitoring data of the system and environmental changes by adopting a self-adaptive control algorithm based on extreme working condition reaction analysis, and matches various working conditions to generate adjusted threshold parameters.
As a further aspect of the invention, the abnormal signal set is specifically abnormal frequency, amplitude variation and time sequence abnormality, the classified fault modes are specifically circuit faults, component loss and connection problems, the optimized diagnosis strategy comprises an optimized fault detection sequence and an improved fault judgment criterion, the causal relation model comprises inter-component fault propagation paths and fault influence degrees, the network interaction graph comprises inter-component interaction strength, hierarchical dependency and potential fault propagation paths, the association rule set comprises co-occurrence frequency and condition dependency rules among components, the extreme working condition reaction analysis is specifically performance and weak points of the system under extreme conditions, and the adjusted threshold parameters comprise fault detection sensitivity and response time threshold adjusted according to environment and operation condition variation.
As a further scheme of the invention, the fault signal identification module comprises a signal acquisition sub-module, a fourier transform sub-module and a signal enhancer module;
The signal acquisition sub-module is based on sensor data of a motorcycle electrical system, adopts an analog-digital conversion technology, captures real-time data by using a signal acquisition circuit, converts an analog signal into a digital signal by digital processing, and generates original signal data;
The Fourier transform submodule is used for converting signals from time to frequency domain by adopting a fast Fourier transform algorithm based on original signal data, extracting frequency spectrums of the signals, analyzing the frequency domain by decomposing the signals into frequency components thereof, identifying key frequency components, analyzing abnormal modes in the signals and generating a frequency domain analysis result;
The signal enhancer module adopts a digital filter technology based on a frequency domain analysis result, removes noise by using a Butterworth low-pass filter, enhances signals in a target frequency range by using a Chebyshev band-pass filter, filters low-frequency interference by using an elliptic high-pass filter, highlights abnormal signal characteristics, and identifies and establishes an abnormal signal set.
As a further scheme of the invention, the fault mode analysis module comprises a fault tree construction sub-module, a mode classification sub-module and a cause analysis sub-module;
The fault tree construction submodule adopts fault tree analysis to construct a logic relation of fault events based on an abnormal signal set, analyzes and determines the possibility of a plurality of fault events through a fault mode and influence, and then uses a logic gate to connect the plurality of fault events to form a fault logic tree;
The pattern classification submodule is based on a fault logic tree, adopts a K-means clustering algorithm to perform feature extraction on fault data, clusters the fault data into differentiated fault patterns by calculating the similarity between fault events, performs feature extraction and similarity measurement on multi-dimensional data, groups similar fault patterns and generates fault pattern types;
The cause analysis submodule analyzes the causes of each type of fault mode by adopting linear regression analysis based on fault mode types, quantifies the correlation among variables by a statistical method, determines key fault causes, and simultaneously identifies and evaluates the correlation between fault causes and influence factors to generate classified fault modes.
As a further scheme of the invention, the genetic algorithm optimization module comprises a strategy generation sub-module, a genetic algorithm realization sub-module and a strategy optimization sub-module;
The strategy generation submodule builds a classification model by analyzing the characteristics and rules of fault data by adopting a decision tree algorithm based on the classified fault modes, and classifies a plurality of fault instances according to the fault characteristics by utilizing a data clustering method to generate an initial diagnosis strategy set;
the genetic algorithm realization submodule adopts a genetic algorithm to perform selection, crossing and mutation operations through a coding strategy set based on an initial diagnosis strategy set, simultaneously adopts a simulated annealing algorithm, and circularly adjusts temperature parameters to capture an optimal solution through randomly selecting the solution and calculating cost to generate an evolution diagnosis strategy set;
The strategy optimization submodule adopts neural network optimization based on an evolutionary diagnosis strategy set, performs training by constructing a multi-layer network structure and inputting diagnosis data, adjusts network weights, and simultaneously combines a Bayesian network, and updates probability distribution of the diagnosis strategy by utilizing probability reasoning to generate an optimized diagnosis strategy.
As a further scheme of the invention, the causal relationship modeling module comprises a Bayesian network construction sub-module, a relationship analysis sub-module and a propagation path evaluation sub-module;
The Bayesian network construction submodule is used for modeling based on an optimized diagnosis strategy by adopting a Bayesian network algorithm, determining the probability distribution of each network node through statistical analysis of data, simultaneously determining the relationship among nodes by utilizing condition dependency analysis, constructing a network structure, carrying out probability calculation and structure learning of batch data, and generating a Bayesian network model;
The relation analysis submodule analyzes the interdependence among the nodes by using a correlation rule mining method based on a Bayesian network model, identifies frequent modes in fault data by using a statistical method, analyzes causal relations among the modes, reveals interaction and influence among components, and generates a component relation analysis result;
The propagation path evaluation submodule evaluates the path of fault propagation by applying a path analysis technology based on the component relation analysis result, determines a key channel and key nodes of fault propagation by calculating a network flow algorithm, and simultaneously analyzes a network topological structure and flow distribution to generate a causal relation model.
As a further scheme of the invention, the multi-layer network analysis module comprises a network construction sub-module, a hierarchical relationship analysis sub-module and an interaction mode evaluation sub-module;
The network construction submodule builds a multi-layer network structure based on a causal relation model by using a graph theory network modeling technology, maps a plurality of components and layers into nodes and edges in a graph theory, establishes the connection among the layers by defining a connection rule among the nodes, and further generates a multi-layer structure network model;
The hierarchical relation analysis submodule is used for analyzing interaction modes and dependency relations among different layers based on a multi-layer structure network model by applying a hierarchical relation analysis technology, identifying key contact points among the layers by utilizing a network analysis method, evaluating interaction strength among the layers, revealing interaction and dependency states of the different layers and generating a hierarchical interaction analysis result;
The interaction mode evaluation submodule analyzes interaction characteristics and modes among different layers by adopting an interaction strength evaluation and key node analysis method based on a hierarchical interaction analysis result, calculates and compares interaction strengths among the multi-level nodes, and identifies key nodes and potential risk areas to generate a network interaction graph.
As a further scheme of the invention, the association rule mining module comprises a data preprocessing sub-module, an Apriori algorithm realization sub-module and a rule evaluation sub-module;
The data preprocessing sub-module is based on a network interaction diagram, adopts a data preprocessing method, eliminates abnormal data points through recognition and processing of abnormal values, unifies the format of a data set, fills or eliminates missing data, and generates a preprocessed data set;
the Apriori algorithm implementation submodule is used for implementing the Apriori algorithm based on the preprocessed data set, analyzing the occurrence frequency of multiple items in the data set, identifying frequent item sets, calculating the support degree and the confidence degree among the multiple item sets, mining and constructing item set relations, and further generating an association rule candidate set;
The rule evaluation submodule performs rule evaluation based on the association rule candidate set, analyzes the support degree and the confidence degree of the rule, tests the rule, screens the association rule from the association rule candidate set based on representativeness and accuracy, and generates the association rule set.
As a further scheme of the invention, the extreme condition analysis module comprises a monte carlo simulation sub-module, a working condition setting sub-module and a stability and weak point evaluation sub-module;
The Monte Carlo simulation submodule adopts a Monte Carlo simulation algorithm based on the association rule set of the extreme condition analysis module, simulates system behaviors and performance reactions by generating batch random samples, performs probability analysis of system performance and response, and generates an extreme working condition performance simulation result;
the working condition setting submodule adopts a decision tree analysis method based on the extreme working condition performance simulation result, establishes a decision model according to data in the result, classifies and carries out regression analysis on the system performance under various working conditions, and simultaneously determines key factors influencing the system performance to generate a refined working condition analysis result;
The stability and vulnerability assessment submodule is used for constructing a differential equation and a state space model reflecting the dynamic response of the system by adopting a dynamic simulation model of the system based on the refined working condition analysis result, assessing the stability and vulnerability of the system under various working conditions and generating extreme working condition response analysis.
As a further scheme of the invention, the dynamic threshold adjustment module comprises an adaptive algorithm realization sub-module, a threshold calculation sub-module and an environment monitoring feedback sub-module;
The self-adaptive algorithm realizes the response analysis of the submodule based on extreme working conditions, adopts a self-adaptive control algorithm, adjusts algorithm parameters by analyzing system states and prediction data in real time, continuously monitors the system states, estimates future behaviors by using a prediction model, and generates a self-adaptive parameter adjustment scheme according to the prediction adjustment control parameters;
the threshold calculation sub-module is based on a self-adaptive parameter adjustment scheme, adopts a dynamic threshold calculation method, combines real-time monitoring data and historical performance indexes, determines an optimal threshold by analyzing the statistical characteristics and the change trend of the data, comprises the analysis of the centralized trend and the distribution characteristic of the data, and generates an optimized threshold parameter based on the continuous optimization of the analysis result on the fault detection threshold;
The environment monitoring feedback submodule is based on optimized threshold parameters, adopts an environment sensing technology and a self-adaptive adjustment strategy, continuously monitors external environment changes, dynamically adjusts the threshold according to the changes, simultaneously utilizes a sensor network to collect environment data, dynamically adjusts a fault detection threshold according to the collected data, matches a differential working condition, and generates adjusted threshold parameters.
Compared with the prior art, the invention has the advantages and positive effects that:
In the invention, the Fourier transform algorithm is higher in efficiency in signal processing, more accurately extracts signal characteristics, effectively removes noise and improves the recognition accuracy of fault signals. The combination of the fault tree analysis and the statistical analysis method ensures that the classification and the reason analysis of fault signals are more detailed, and the recognition capability of fault modes is enhanced. The introduction of the genetic algorithm optimizes the fault diagnosis strategy, so that the diagnosis process is more efficient, and the maintenance time and cost are reduced. The use of bayesian networks provides insight into causal analysis to help reveal potential causes and propagation paths of faults. The application of multi-layer network analysis and graph theory allows a more comprehensive understanding of the complex structure of the electrical system and helps to prevent system-level faults. The use of the Apriori algorithm is more efficient in association rule mining, helping to identify potential false triggers. The application of the Monte Carlo simulation method provides analysis of system performance under extreme working conditions, and enhances the reliability of the system. The adoption of the self-adaptive control algorithm enables the fault detection threshold to be dynamically adjusted according to real-time data and environmental changes, and improves the adaptability and accuracy of the system.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1
Referring to fig. 1 to 2, the motorcycle electrical system fault detection system includes a fault signal recognition module, a fault mode analysis module, a genetic algorithm optimization module, a causal relationship modeling module, a multi-layer network analysis module, a correlation rule mining module, an extreme condition analysis module, and a dynamic threshold adjustment module;
The fault signal recognition module is used for carrying out signal processing by adopting a Fourier transform algorithm based on sensor data of the motorcycle electrical system, extracting signal characteristics, removing noise through a filter, recognizing and establishing an abnormal signal set;
The fault mode analysis module classifies and analyzes reasons of the differentiated fault signals by adopting a fault tree analysis and statistical analysis method based on the abnormal signal set to generate classified fault modes;
the genetic algorithm optimization module optimizes the fault diagnosis strategy by utilizing a genetic algorithm based on the classified fault modes, and generates an optimized diagnosis strategy through iterative adjustment of a diagnosis flow and automatic optimization of parameters;
the causal relation modeling module is used for modeling and analyzing causal relation among components in the electrical system by adopting a Bayesian network based on an optimized diagnosis strategy, revealing potential reasons and propagation paths of faults and generating a causal relation model;
The multi-layer network analysis module is used for analyzing a multi-layer structure of the electrical system by utilizing a graph theory based on a causal relation model, revealing interaction and dependency between different layers and generating a network interaction graph;
the association rule mining module analyzes interaction data among components by adopting an Apriori algorithm based on a network interaction graph, and identifies potential association modes and fault trigger factors to generate an association rule set;
the extreme condition analysis module simulates the performance of the electric system under the extreme working condition by adopting a Monte Carlo simulation method based on the association rule set, recognizes the response of the system under the extreme environment, and generates extreme working condition reaction analysis;
the dynamic threshold adjustment module dynamically adjusts the fault detection threshold according to real-time monitoring data of the system and environmental changes by adopting a self-adaptive control algorithm based on extreme working condition reaction analysis, and matches various working conditions to generate adjusted threshold parameters.
The abnormal signal set is abnormal frequency, amplitude change and time sequence, the classified fault modes are circuit faults, component loss and connection problems, the optimized diagnosis strategy comprises an optimized fault detection sequence and an improved fault judgment criterion, the causal relation model comprises a fault propagation path and a fault influence degree among components, the network interaction graph comprises interaction strength among components, hierarchical dependency and potential fault propagation paths, the association rule set comprises co-occurrence frequency among components and condition dependency rules, the extreme working condition reaction analysis is particularly performance and weak points of the system under extreme conditions, and the adjusted threshold parameters comprise fault detection sensitivity and response time threshold adjusted according to environment and operation condition changes.
In the fault signal recognition module, operation is performed by sensor data of the motorcycle electrical system. Specifically, the module processes the signals captured by the sensors using a fourier transform algorithm that converts the time domain signals to the frequency domain in order to identify and analyze the frequency components in the signals. After that, the module removes noise in the signal through the designed filter, and accuracy and reliability of data are improved. Signal characteristics, such as frequency and amplitude, become more apparent after these treatments, thereby facilitating subsequent analysis. The finally established abnormal signal set of the module comprises abnormal frequency, amplitude variation, time sequence abnormality and the like, and accurate basic data is provided for fault detection, so that the accuracy and the efficiency of fault identification are greatly improved.
In the fault mode analysis module, fault tree analysis and statistical analysis methods are applied to classify and analyze reasons for fault signals based on the abnormal signal set. The fault tree analysis decomposes the fault cause into finer events and conditions through the logic tree structure, so that the source and the propagation path of the fault are clearer. The statistical analysis reveals the characteristics and rules of different fault modes by calculating and comparing the statistical characteristics of fault data, such as average value, variance and the like. The output of the module comprises the classified fault modes such as circuit faults, component loss, connection problems and the like, and clear guidance is provided for subsequent fault diagnosis and maintenance.
In the genetic algorithm optimization module, a genetic algorithm is utilized to optimize a fault diagnosis strategy. The algorithm simulates selection, crossover and mutation mechanisms in the biological evolution process, thereby optimizing the fault detection sequence and decision criteria. Through continuous iteration, the module adjusts the diagnosis flow and parameters so as to achieve higher fault detection efficiency and accuracy. The genetic algorithm evaluates the effectiveness of various strategies through fitness functions in the process, and selects the optimal strategy for crossover and mutation to generate a new diagnosis strategy. The self-iteration and optimization mechanism ensures that the fault diagnosis strategy can adapt to various complex scenes, and the finally generated optimization diagnosis strategy comprises an optimal fault detection sequence and an improved fault judgment standard, so that the efficiency and the accuracy of fault diagnosis are obviously improved.
In the causal relationship modeling module, a Bayesian network is adopted to model and analyze causal relationships among internal components of the electrical system. The Bayesian network is a probabilistic graph model capable of expressing complex conditional dependencies and uncertainties among components. By analyzing the fault data and system structure, the module builds a model that represents the fault propagation paths and the extent of impact between the components. The potential failure cause and the propagation path of the system are clearly revealed, and a powerful analysis tool is provided for failure prevention and maintenance.
In the multilayer network analysis module, the multilayer structure of the electrical system is analyzed by using graph theory. The module exposes interactions and dependencies between different hierarchical components by building a network model. Nodes in the network represent individual components of the system, while edges represent connections and interactions between components. By analyzing the structure and connection patterns of the network, key components and potential failure points in the system can be identified, as well as the dependency and interaction strength between different levels. Such analysis helps to understand the overall structure of the electrical system and potential fault propagation pathways, providing insight into the optimization and maintenance of the system.
In the association rule mining module, interaction data among components is analyzed by adopting an Apriori algorithm based on a network interaction graph. The Apriori algorithm is a common association rule mining method, and the association rule in the data is found by calculating the frequency of a term set. In the context of electrical systems, the algorithm is capable of identifying co-occurrence frequencies and condition-dependent rules among components, revealing potential failure triggers and modes of interaction among components. By the method, the module can not only identify the current fault mode, but also predict potential fault risks, and provides important data support for preventive maintenance of the system.
In the extreme condition analysis module, the performance of the electrical system under extreme operating conditions is simulated by a Monte Carlo simulation method. Monte Carlo simulation is a random sampling based computing technique that can simulate various complex and uncertain system behaviors. In this module, the response of the electrical system under different environmental and operating conditions is simulated, identifying the vulnerability and performance behavior of the system under extreme conditions. Such simulation helps engineers understand and predict the behavior of the system under non-standard conditions, thereby providing a more comprehensive view of system design and maintenance.
In the dynamic threshold adjustment module, a self-adaptive control algorithm is adopted to dynamically adjust the fault detection threshold according to the real-time monitoring data and environmental change of the system. This method allows the system to automatically adjust its fault detection parameters, such as fault detection sensitivity and response time thresholds, based on current operating conditions and environmental factors. The dynamic adjustment mechanism enables the fault detection system to be more accurately adapted to different working conditions, improves the reliability and adaptability of the system, and ensures that faults can be effectively identified and prevented under various environments and operating conditions.
Referring to fig. 3, the fault signal identifying module includes a signal acquisition sub-module, a fourier transform sub-module, and a signal enhancer module;
the signal acquisition sub-module is based on sensor data of a motorcycle electrical system, adopts an analog-digital conversion technology, captures real-time data by using a signal acquisition circuit, converts an analog signal into a digital signal by digital processing, and generates original signal data;
The Fourier transform submodule converts the signal from time to frequency domain by adopting a fast Fourier transform algorithm based on the original signal data, extracts the frequency spectrum of the signal, analyzes the frequency domain by decomposing the signal into frequency components thereof, identifies key frequency components, analyzes abnormal modes in the signal and generates a frequency domain analysis result;
The signal enhancer module adopts a digital filter technology based on a frequency domain analysis result, removes noise by using a Butterworth low-pass filter, enhances signals in a target frequency range by using a Chebyshev band-pass filter, filters low-frequency interference by using an elliptic high-pass filter, highlights abnormal signal characteristics, and identifies and establishes an abnormal signal set.
In a signal acquisition sub-module of the motorcycle electrical system, an analog-digital conversion technology is adopted, and the specific process is to capture real-time data based on sensor data. The sensor data, typically in the form of voltages or currents, are first captured by a signal acquisition circuit. The circuit comprises an operational amplifier and a filter, so that the stability and accuracy of signals in the acquisition process are ensured. The analog signal is then converted to a digital signal using an analog-to-digital converter (ADC). In the conversion process, the ADC samples the signal periodically according to a specific sampling rate, converts the analog value of each sampling point into a corresponding digital representation, which includes quantization and encoding, wherein the quantization determines the amplitude levels of the signal, and the encoding converts these levels into a binary format. Through this series of operations, raw signal data is generated, which is stored in binary format, representing the physical quantity measured by the sensor.
The fourier transform submodule performs a Fast Fourier Transform (FFT) algorithm based on these raw signal data. The FFT algorithm first breaks down the time series data into its frequency components, which involves mapping each data point onto a complex plane of different frequencies. The algorithm optimizes the calculation process in an iterative manner to quickly obtain the spectrum representation of the time domain signal. This conversion reveals the frequency composition of the signal, including the frequency components dominant in the signal and its strength. Further, by frequency domain analysis, abnormal patterns in the signal, such as prominent frequency peaks or irregular waveforms, can be identified. By analysing these abnormal frequency components, the FFT submodule is able to generate detailed frequency domain analysis results which are presented in the form of a spectrogram, intuitively showing the relative intensities of the different frequency components.
The signal enhancement submodule further processes the frequency domain analysis results. In this sub-module a series of digital filters are used to process the signal. First, a butterworth low-pass filter is used to remove high-frequency noise, which is characterized by providing a smooth transition at the cut-off frequency, reducing signal distortion. The chebyshev bandpass filter is then used to enhance the signal in a specific frequency range. Such a filter precisely enhances the target frequency component while suppressing other frequencies by adjusting the bandwidth and center frequency. Finally, an elliptical high pass filter is used to filter low frequency interference, the design of which allows signals below the cut-off frequency to be effectively attenuated while preserving the integrity of the high frequency signals. During these filtering processes, the enhancement and suppression degree of the signal can be precisely controlled by adjusting parameters of the filter, such as cut-off frequency and bandwidth. Such signal processing not only highlights abnormal signal features, but also helps identify and build up abnormal signal sets, which is critical for fault diagnosis and preventative maintenance. At the final stage of the signal enhancer module, the filtered signal is further analyzed to identify and classify abnormal patterns. Through these operations, the sub-module is able to generate a clear set of signal features reflecting the operating conditions of the motorcycle electrical system. For example, if the detected frequency pattern matches a known fault signature, the system may automatically identify potential problems and prompt further inspection or maintenance. The results of these enhancements and analyses are ultimately stored in digital format as the basis for fault detection and diagnosis.
Consider a specific motorcycle electrical system fault detection scenario. It is assumed that during daily operation, raw voltage data captured by the sensor shows an abnormal fluctuation during engine start-up, where some data points represent abnormally high voltage values. In the signal acquisition sub-module, these analog data are converted to a digital format, e.g., mapping the voltage range of 0 to 5 volts to integer values of 0 to 1023. In the fourier transform sub-module, these time series data are converted into a frequency spectrum by an FFT algorithm, revealing the frequency components of the voltage fluctuations during engine start-up. For example, analysis shows a significant peak at a particular frequency, indicating that the electrical system is abnormal in that frequency range. These anomalies are caused by loose wires or damaged components. Next, in the signal enhancer module, the high frequency noise in the signal is first removed by the butterworth low pass filter, which helps to more clearly identify and analyze the critical frequency components. A chebyshev band pass filter is then used, with emphasis on enhancing the signal in the frequency range where anomalies are detected. Finally, the elliptical high-pass filter removes low-frequency interference, so that the abnormal signal characteristics are more prominent. By these refined filtering operations, critical information in the signal is enhanced while uncorrelated or interfering signal components are suppressed. Finally, the processed signal data is used to identify failure modes. For example, if the detected abnormal frequency pattern matches a particular fault type recorded in the database, the system may identify a potential fault, such as a battery under-charge or an engine over-temperature. This matching and identification process relies on detailed spectral analysis and a previously established failure mode database. Finally, this process generates a report containing the type of potential fault and related data, for example, shown as "abnormal voltage fluctuation at engine start, frequency XX hertz, for the reason: low battery voltage or loose electrical connection).
Referring to fig. 4, the fault mode analysis module includes a fault tree construction sub-module, a mode classification sub-module, and a cause analysis sub-module;
The fault tree construction submodule adopts fault tree analysis to construct a logic relation of fault events based on an abnormal signal set, analyzes and determines the possibility of a plurality of fault events through a fault mode and influence, and then uses a logic gate to connect the plurality of fault events to form a fault logic tree;
The mode classification submodule is based on a fault logic tree, adopts a K-means clustering algorithm to perform feature extraction on fault data, clusters the fault data into differentiated fault modes by calculating the similarity between fault events, performs feature extraction and similarity measurement on multidimensional data, groups similar fault modes and generates fault mode categories;
The cause analysis submodule analyzes the causes of each type of fault mode by adopting linear regression analysis based on fault mode types, quantifies the correlation among variables by a statistical method, determines key fault causes, and simultaneously identifies and evaluates the correlation between fault causes and influence factors to generate classified fault modes.
In a fault tree construction sub-module of a fault detection system of a motorcycle electrical system, a logic relation of fault events is constructed by a fault tree analysis method, wherein the process starts with an abnormal signal set, and the abnormal signal set comprises a series of recognized fault signal characteristics, such as specific frequency fluctuation, voltage abnormality and the like. These data are stored in a digital format, such as binary codes or floating point numbers. The first step in fault tree analysis is to perform fault pattern and impact analysis on these anomaly signals to determine the likelihood of each fault event. In this process, each fault event is defined as a node, and the potential fault cause and influence of each node are identified through the feature analysis of each node. These fault events are then connected by logic gates (e.g., AND, OR gates) to form a comprehensive fault logic tree. The use of logic gates is based on causal relationships between fault events, such as one fault event being a direct result or requirement of another fault event. By comprehensively analyzing and connecting the fault events, a fault tree which comprehensively reflects potential faults of the system and interrelations thereof is constructed. The fault tree not only reveals single fault events, but also shows interaction and logic relation among the fault events, thereby providing important basis for further fault diagnosis and prevention.
The mode classification submodule analyzes fault data extracted from the fault logic tree through a K-means clustering algorithm. The K-means clustering algorithm first randomly selects K initial cluster centers and then assigns them to the nearest cluster centers according to the similarity between fault events. The similarity measure here is based on multidimensional characteristics of the fault data, such as frequency, voltage level, duration, etc. The algorithm continuously adjusts the position of the clustering center through an iterative process until the optimal clustering effect is achieved. In each iteration, the algorithm calculates the distance of each failed data point from each cluster center, assigns the data point to the nearest cluster center, and then recalculates the center of each cluster. This process is repeated until the position of the cluster center is stable or a preset number of iterations is reached. In this way, the algorithm effectively classifies fault data into different categories, each representing a unique failure mode. For example, some failure modes are related to voltage fluctuations, while other modes are related to frequency anomalies. The result of K-means clustering is a series of different failure mode categories, each containing failure events with similar characteristics. The classification not only helps to understand the fault mode more clearly, but also provides a basis for further fault cause analysis and prevention strategy formulation.
The cause analysis submodule adopts linear regression analysis to explore the potential cause of each failure mode based on the failure mode categories. Linear regression analysis aims to quantify the correlation between variables by statistical methods, thereby determining which factors are critical factors leading to a particular failure mode. In this process, the characteristics of each failure mode category (e.g., frequency fluctuations, voltage changes, etc.) are considered as independent variables, while the severity or frequency of occurrence of the failure is taken as a dependent variable. By performing a linear regression analysis on this data, the module is able to identify variables that are strongly related to the failure mode, which are the primary cause of the failure. In addition, the sub-module evaluates the correlation between different fault causes and how to affect the occurrence of the fault together. For example, linear regression reveals that under certain operating conditions, voltage fluctuations and frequency anomalies together increase the risk of some type of failure occurring. Through such detailed analysis, the cause analysis sub-module is able to generate categorized fault pattern reports detailing the primary cause and related factors of each type of fault pattern, providing a scientific basis for fault prevention and maintenance decisions.
It is assumed that the motorcycle electrical system detects a series of abnormal signals during operation, including sudden voltage drops and irregular fluctuations in frequency. In the fault tree construction submodule, these anomaly signals are encoded in a digital format, for example, the voltage drops are represented as a continuous sequence of digital values, and the frequency fluctuations are represented as their corresponding spectral data. Through fault tree analysis, these anomaly signals are linked into a logical structure revealing fault paths and causal relationships. For example, a voltage drop results in a decrease in the performance of an Electronic Control Unit (ECU), which in turn causes frequency fluctuations. In the pattern classification sub-module, the fault data are classified into different categories by a K-means clustering algorithm. Setting the number of initial cluster centers as 3, and classifying the fault event by calculating Euclidean distance between each fault data point and the cluster center by the algorithm. After multiple iterations, three distinct failure mode categories are formed: one class focuses on voltage drop events, another class focuses on frequency fluctuations, and a third class is the combined effect of voltage drop and frequency fluctuations. This classification reveals the different patterns and characteristics of the fault event, providing important information for a thorough understanding of the cause of the fault. In the cause analysis sub-module, linear regression analysis is performed for each type of failure mode. For example, for failure modes related to voltage drop, linear regression analysis considers a number of factors such as battery charge, temperature, run time, etc., and evaluates the relationship between these factors and the frequency of failure occurrence. The analysis results show that low battery power and high temperature are the main causes of voltage drop. Such analysis provides specific guidance for maintenance and fault prevention of the motorcycle, indicating that significant attention is required to the battery status and performance of the heat dissipating system. By the method, the cause analysis submodule not only identifies the cause of the fault, but also evaluates interaction among various factors and provides a comprehensive fault analysis result.
Referring to fig. 5, the genetic algorithm optimization module includes a policy generation sub-module, a genetic algorithm implementation sub-module, and a policy optimization sub-module;
The strategy generation submodule builds a classification model by analyzing the characteristics and rules of fault data by adopting a decision tree algorithm based on the classified fault modes, and classifies a plurality of fault instances according to the fault characteristics by utilizing a data clustering method to generate an initial diagnosis strategy set;
The genetic algorithm realization submodule adopts a genetic algorithm based on an initial diagnosis strategy set, performs selection, crossing and mutation operations through a coding strategy set, simultaneously adopts a simulated annealing algorithm, and captures an optimal solution through randomly selecting the solution and calculating the cost, and circularly adjusts temperature parameters to generate an evolution diagnosis strategy set;
The strategy optimization submodule adopts neural network optimization based on an evolutionary diagnosis strategy set, performs training by constructing a multi-layer network structure and inputting diagnosis data, adjusts network weights, and simultaneously combines a Bayesian network, and updates probability distribution of the diagnosis strategy by utilizing probabilistic reasoning to generate an optimized diagnosis strategy.
In a strategy generation sub-module of the motorcycle electrical system fault detection system, a classification model is constructed to analyze the characteristics and rules of fault data through a decision tree algorithm. The process begins with processed fault data, which typically includes, but is not limited to, the time at which the fault occurred, the duration, the affected system components, and any associated sensor readings, such as voltage or temperature changes. These data are encoded into a structured format, such as CSV or database tables, for algorithmic processing. The decision tree algorithm first selects a feature as the root node and then segments the dataset into subsets based on this feature. This partitioning is based on differences in eigenvalues so that each subset is as consistent as possible across the target variables (i.e., fault types). For example, if voltage drop is a critical factor leading to some type of failure, the algorithm may use voltage characteristics to segment at the top level of the decision tree. This process is then repeated for each subset, selecting new features for further segmentation until each subset is sufficiently consistent over the target variable or reaches a predefined depth limit. Furthermore, in conjunction with the data clustering method, multiple fault instances are categorized according to fault characteristics, which facilitates the discovery of patterns and relationships in the fault data. Through these operations, an initial diagnostic strategy set is generated, which is stored in the form of a decision tree and provides a basis for subsequent fault diagnosis and prevention.
The genetic algorithm implementation submodule then optimizes this initial set of diagnostic strategies. First, a strategy set is encoded into a set of initial populations by a specific encoding method (e.g., binary encoding). Then, a new set of strategies is generated using the selection, crossover and mutation operations of the genetic algorithm. In the selection process, the strategy for the subsequent generation is selected according to the fitness of each strategy (i.e., its measure of fault diagnosis effectiveness). Crossover operations simulate chromosome exchange in biological genetics, and new strategies are generated by combining features of the two strategies. The mutation operation then randomly alters a portion of certain strategies to introduce new characteristics and diversity. Meanwhile, in combination with the simulated annealing algorithm, the search process is optimized by randomly selecting a solution at each step and calculating its cost. The simulated annealing algorithm allows larger search space and solution change in the initial stage, and the search range is gradually reduced and tends to be stable along with the reduction of the temperature parameter. The method combining the genetic algorithm and the simulated annealing algorithm enables the strategy set to effectively search for the optimal solution while maintaining diversity. This process ultimately produces an evolutionary diagnostic strategy set that is more sophisticated and efficient and can provide more accurate fault diagnosis and prevention recommendations.
The policy optimization submodule further improves the performance and accuracy of the policies. The neural network optimization method is adopted, and training is performed by constructing a multi-layer network structure and inputting diagnostic data. During training, the weights and bias of the network are adjusted based on the data to reduce the gap between the predicted and actual results. For example, if a policy exhibits a high error in identifying a particular type of fault, the neural network may optimize performance by adjusting weights related to the policy. Meanwhile, the Bayesian network is combined, and probability distribution of the diagnosis strategy is updated by utilizing probability reasoning. Bayesian networks provide a method to take into account uncertainty and causal relationships to optimize the decision and decision process of policies by calculating conditional probabilities between various fault causes and characterizations. The method combining the neural network and the Bayesian network not only improves the accuracy of the strategy, but also increases the capability of adapting to different fault conditions. The finally generated optimized diagnosis strategy has higher reliability and effectiveness, and can provide more accurate guidance for fault detection and maintenance of the motorcycle electrical system.
It is assumed that a series of faults are detected in the motorcycle electrical system, including a drop in battery voltage, sensor signal anomalies, and the like. The strategy generation submodule firstly analyzes the fault data through a decision tree algorithm to construct a preliminary fault diagnosis model. For example, if a battery voltage drop is often accompanied by a particular sensor signal anomaly, the decision tree takes these two features as key nodes for distinguishing fault types. Meanwhile, the data clustering method helps to classify similar fault instances into one type, and an initial diagnosis strategy set is formed. Next, the genetic algorithm implementation submodule optimizes these policies. The initial set of policies is assumed to include various fault types and corresponding diagnostic steps, which policies are encoded into individuals in a genetic algorithm. Through selection, crossover and mutation operations, these strategies continue to evolve, generating new, more accurate diagnostic strategies. Meanwhile, randomness is introduced in the optimization process of the simulated annealing algorithm, so that the problem of local optimal solution is avoided, and the known global optimality is ensured. In this way, a set of more efficient evolutionary diagnostic strategies is formed. Finally, the strategy optimization submodule further improves the performance of the strategy through a neural network and a Bayesian network. The neural network continuously adjusts the weights through the training process to better match the fault data with the expected results. For example, neural networks find that when a battery voltage and some sensor signal are abnormal at the same time, the fault type is typically a battery fault, and thus the weights are adjusted to reflect this finding. Meanwhile, the Bayesian network provides a method for processing uncertainty and complex relation through probabilistic reasoning, so that the strategy can make more accurate judgment when facing different types of faults. Thus, by combining the neural network and the Bayesian network, an optimized set of diagnostic strategies is generated, which not only accurately identify fault types, but also provide targeted repair suggestions and preventive measures.
Referring to fig. 6, the causal relationship modeling module includes a bayesian network construction sub-module, a relationship analysis sub-module, and a propagation path evaluation sub-module;
The Bayesian network construction submodule is used for modeling based on an optimized diagnosis strategy by adopting a Bayesian network algorithm, determining the probability distribution of each network node through statistical analysis of data, simultaneously determining the relationship among nodes by utilizing condition dependency analysis, constructing a network structure, carrying out probability calculation and structure learning of batch data, and generating a Bayesian network model;
The relation analysis submodule analyzes the interdependence among the nodes by using a correlation rule mining method based on a Bayesian network model, identifies frequent patterns in fault data by using a statistical method, analyzes causal relations among the patterns, reveals interaction and influence among components, and generates a component relation analysis result;
the propagation path evaluation submodule evaluates the path of fault propagation by applying a path analysis technology based on the component relation analysis result, determines a key channel and key nodes of fault propagation by calculating a network flow algorithm, and simultaneously analyzes a network topological structure and flow distribution to generate a causal relation model.
In the Bayesian network construction submodule, a specific data set is modeled through a Bayesian network algorithm, and a diagnosis strategy based on probabilistic reasoning is realized. This process involves collecting data and formatting it into a format suitable for algorithmic processing, such as a structured tabular form, containing relevant variables and observations. Bayesian network algorithms first determine the probability distribution of each node in the network through data statistical analysis. For example, for fault diagnosis of an electrical system, the dataset contains parameters such as voltage, current, temperature, etc., each constituting a node whose probability distribution of values is calculated from historical data. Furthermore, the algorithm also uses conditional dependency analysis to determine relationships between nodes. This involves calculating conditional probabilities between different nodes to determine if causal relationships exist between each other. For example, if an abnormal increase in current is always accompanied by a decrease in voltage, then there is a dependency between the two nodes. The algorithm then builds a network structure by identifying these dependencies and expressing them as connections in the network. The next step of the algorithm is to make probabilistic calculations and structure learning of the batch data. At this stage, the algorithm analyzes a larger-scale dataset, and gradually optimizes the network structure through an iterative process to more accurately reflect the relationships between nodes. This process involves techniques such as Expectation Maximization (EM) algorithms for handling uncertainty and missing values in the data. Ultimately, bayesian network model generation is complete, which can reveal complex interactions between different system components, as well as the probability of various events occurring under certain conditions. Such models may be used to predict the behavior of the system, identify potential points of failure, or provide guidance for maintenance work.
In the relation analysis submodule, based on the Bayesian network model, the inter-dependency relation among the nodes is deeply analyzed by using a correlation rule mining method, and the process firstly needs to convert the data in the Bayesian network model into a format suitable for the correlation rule mining, for example, discretizing continuous variables so as to better identify frequently-occurring modes. Next, a set of items that frequently occur in the fault data is identified using an association rule mining technique, such as the Apriori algorithm. For example, in electrical system fault data, algorithms find that both high temperature and current anomalies are often present at the same time. The algorithm further analyzes the causal relationships between the patterns, revealing interactions and influences between the system components. This involves calculating the probability of occurrence of different pattern combinations and their strength of association with system faults. For example, if a combination of high temperature and current anomalies is found to be highly correlated with the occurrence of an electrical fault, such a pattern is considered to be of great diagnostic value. Such analysis helps understand the dynamic relationships between components and provides critical information for fault diagnosis and prevention. After the relation analysis is completed, the component relation analysis results generated by the submodules not only show the mutual influence among all the components in detail, but also reveal potential fault trigger factors. These results are critical to the maintenance team, and maintenance strategies can be adjusted based on these information to prevent failures from occurring, thereby improving the reliability and efficiency of the system.
In the propagation path evaluation sub-module, path analysis techniques are applied to evaluate the path of fault propagation, based on component relationship analysis results, which first need to be converted into a format suitable for path analysis, such as a graph or network structure, where nodes represent individual components in the system and edges represent interactions between components. Next, the critical channels and critical nodes for fault propagation are calculated using a network flow algorithm, such as a shortest path algorithm or a maximum flow minimum theorem. These algorithms determine the path and key impact points of fault propagation by analyzing the topology and traffic distribution of the network. For example, if a fault is found in the fault analysis of an electrical system, typically starting with a particular component and then propagating through a particular path to the entire system, this component and path are marked as critical points. Such analysis is critical to understanding how faults spread in the system, helping to take targeted measures to prevent further spread of faults. Finally, the causal relationship model generated by the submodule provides a detailed view of fault propagation, revealing potential weaknesses in the system. This is critical to the design of more efficient fail-safe measures and maintenance strategies, helping to promote overall stability and safety of the system.
In a motorcycle electrical system fault detection system, it is assumed that data items include engine temperature, battery voltage, throttle position, and brake system status, each having an analog value. Through machine learning algorithms, these data are first used to train a bayesian network model. For example, engine temperature remains within a certain range during normal operation and may rise significantly during fault conditions. Variations in battery voltage also indicate certain problems with the electrical system. By statistically analyzing these data, the probability distribution of each node (e.g., engine temperature, battery voltage) can be determined, and the relationship between these nodes can be established by a condition-dependent analysis. In the relationship analysis sub-module, a correlation rule mining method is applied to analyze whether there is an abnormal drop pattern in battery voltage when, for example, the engine temperature increases. The discovery of these patterns helps to identify early signs of failure, taking precautions ahead of time. In this way, a causal relationship such as engine overheating that results in a decrease in battery performance can be revealed. The propagation path evaluation sub-module further analyzes the relationships to determine a fault propagation path. For example, if a battery voltage anomaly is found to be typically accompanied by a throttle response problem, then it may be inferred that the fault was initiated from the battery, through the electrical system, and to the throttle control system. By utilizing the network flow algorithm, the critical path and the critical node of fault propagation can be determined, and a basis is provided for formulating a coping strategy.
Referring to fig. 7, the multi-layer network analysis module includes a network construction sub-module, a hierarchical relationship analysis sub-module, and an interaction pattern evaluation sub-module;
The network construction submodule builds a multi-layer network structure based on a causal relation model by using a graph theory network modeling technology, maps a plurality of components and layers into nodes and edges in a graph theory, establishes the connection among the layers by defining a connection rule among the nodes, and further generates a multi-layer structure network model;
The hierarchical relation analysis submodule is used for analyzing interaction modes and dependency relations among different layers based on a multi-layer structure network model, and meanwhile, a network analysis method is used for identifying key contact points among the layers, evaluating interaction strength among the layers, revealing interaction and dependency states of the different layers and generating a hierarchical interaction analysis result;
The interaction mode evaluation submodule analyzes interaction characteristics and modes among different layers by adopting an interaction strength evaluation and key node analysis method based on the layer interaction analysis result, calculates and compares interaction strengths among the multiple layers of nodes, and identifies key nodes and potential risk areas to generate a network interaction diagram.
In the network construction submodule, a multi-layer network structure is constructed through graph theory network modeling technology, so that detailed mapping of each component and hierarchy in a complex system is realized, and the process involves mapping a plurality of components and hierarchies of the system into nodes and edges in graph theory. First, each component or hierarchy is defined as a node, and the relationships between each other are represented as edges. For example, in a motorcycle electrical system, a battery, engine, sensor, etc. may be provided as separate nodes, with the electrical connection between each other forming a side. The data format is typically a structured data table, with each row representing a node (e.g., component) and containing various parameters and attributes associated with that node. The connection rules between nodes are defined based on the actual structural and functional relationships of the system. For example, if a sensor is responsible for monitoring battery voltage, there will be an edge between the two nodes. In constructing a multi-layer network structure, the algorithm may consider the links between the different layers. Herein, hierarchy refers to different functional areas or system modules. By defining connection rules between tiers, algorithms can establish relationships between these multiple tiers, which can involve complex graph-theory algorithms, such as community-detection algorithms, for identifying closely connected groups of nodes in a network that represent different tiers in the system. The resulting multi-layer structural network model not only provides a detailed view between the components of the system, but also reveals complex interactions between the different layers. Such models are critical to understanding the overall structural and functional relationships of the system, helping to identify potential failure points and optimize the system design.
In the hierarchical relationship analysis sub-module, based on a multi-layer structure network model, a hierarchical relationship analysis technology is applied to deeply analyze interaction modes and dependency relationships among different layers, and the process firstly needs to convert data in the network model into a format suitable for hierarchical relationship analysis. For example, the network model may be converted into a tree structure or hierarchy to more intuitively expose relationships between different hierarchies. The key contact points between the layers are then identified using network analysis methods, such as centrality analysis. Centrality analysis may reveal which nodes occupy a central role in the network, having a significant impact on the connectivity and information flow of the overall network. For example, if a certain sensor node has a high centrality in the multi-tier network of the electrical system, it is a critical monitoring point or potential source of failure. Through the analysis, the interaction strength between different levels can be evaluated, and the interaction and dependency states of different levels are revealed. The generated hierarchy interaction analysis result not only shows the complex relationship among the hierarchies in the system in detail, but also provides key insight for understanding how the hierarchies commonly affect the overall function of the system. This is critical to designing a more efficient and robust system, and can help engineers and technicians optimize the system architecture, improving the accuracy and efficiency of fault diagnosis.
In the interaction mode evaluation sub-module, based on the hierarchical interaction analysis result, interaction characteristics and modes among different layers are deeply analyzed by adopting an interaction strength evaluation and key node analysis method, and the process firstly involves calculating interaction strength among multi-level nodes. The data format used is typically a numerical representation of the frequency and strength of interactions between nodes, which can be obtained from system logs, sensor readings, or operational records. The algorithm calculates the interaction strength between the nodes through the data, such as using correlation analysis or mutual information measurement and other methods to determine the interaction strength and importance between the nodes. In addition, the analysis method includes identifying key nodes and potential risk areas in the network. This typically involves using network topology analysis tools, such as network robustness analysis, to identify nodes in the network that are critical to system stability and function. For example, if a node fails in an electrical system, which results in a decrease in overall system performance, that node is considered a critical node. The generated network interaction graph provides an intuitive view showing interaction patterns between different layers and the positions of key nodes. This is useful for identifying potential weak points in the system and optimizing the system design. Engineers can use this information to enhance the reliability of the system, preventing potential fault propagation through reinforcement or redesign of critical nodes.
In a motorcycle electrical system fault detection system, data items include engine temperature, battery voltage, throttle position, brake system status, etc., each item of data having an analog value. For example, engine temperature is typically between 70-90 degrees celsius, battery voltage is between 12-14 volts, throttle position is expressed as a value from 0% (closed) to 100% (fully open), and brake system status may be expressed as a pressure value. The behavior pattern can be established by performing feature analysis on the time series data through a machine learning algorithm. For example, algorithms recognize frequent changes in throttle position under certain specific conditions, such as when engine temperature increases, which are indicative of potential system problems. Next, the pattern recognition process is optimized using data mining techniques, such as cluster analysis or anomaly detection algorithms. These techniques are able to extract meaningful patterns from a large amount of data, such as finding a high probability of failure occurrence at a particular battery voltage and throttle position combination. The machine learning algorithms used in these analysis processes require precise adjustment of parameters such as the depth of the decision tree, the number of clusters in the clustering algorithm, or the threshold of the anomaly detection algorithm to ensure that the model can accurately identify and interpret patterns in the data. The selection and adjustment of these parameters is typically based on cross-validation and model performance assessment to ensure accuracy and generalization capability of the final model.
Referring to fig. 8, the association rule mining module includes a data preprocessing sub-module, an Apriori algorithm implementation sub-module, and a rule evaluation sub-module;
The data preprocessing sub-module adopts a data preprocessing method based on a network interaction diagram, eliminates abnormal data points through recognition and processing of abnormal values, unifies the format of a data set, fills or eliminates missing data, and generates a preprocessed data set;
The Apriori algorithm implementation submodule is used for implementing the Apriori algorithm based on the preprocessed data set, analyzing the occurrence frequency of multiple items in the data set, identifying frequent item sets, calculating the support degree and the confidence degree among the multiple item sets, mining and constructing item set relations, and further generating an association rule candidate set;
The rule evaluation sub-module performs rule evaluation based on the association rule candidate set, analyzes the support degree and the confidence degree of the rule, tests the rule, screens association rules from the association rule candidate set based on representativeness and accuracy, and generates an association rule set.
In the data preprocessing sub-module, the data of the network interaction graph is subjected to refinement processing by a data preprocessing method so as to ensure the quality and consistency of the data. Data preprocessing is a key step of any data analysis project, and directly affects the effect and accuracy of subsequent algorithms. This process includes several key operations, identification and handling of outliers, unification of data set formats, and handling of missing data. First, the identification and processing of outliers is accomplished through various statistical methods. This includes calculating statistical indicators of mean, median, standard deviation, etc. of the data and identifying outliers using a bin graph or Z-score method. For example, in analyzing motorcycle electrical system data, if a reading of a certain sensor is far from the normal range of other similar sensors, then this reading is marked as an outlier. For these outliers, a deletion may be selected or replaced with an average, median, or other reasonable estimate. Second, format unification of a data set refers to converting data into a standard and consistent format for subsequent processing. This includes normalization or normalization of the data, comparing different data sets at the same scale, or converting the classified data into numeric data. Finally, processing missing data is accomplished by padding or culling. Filling of missing data uses average, median, most frequent values or predicted values based on other variables. In some cases, if the missing data is too much, the corresponding record is selected for culling. After these steps are completed, the resulting preprocessed data set will be of higher quality and consistency, laying a solid foundation for subsequent data analysis.
In the Apriori algorithm implementation submodule, an Apriori algorithm is implemented to mine association rules in data based on a preprocessed data set. The Apriori algorithm is a classical data mining algorithm that discovers frequent item sets from a large amount of data and builds association rules. The specific implementation process of the algorithm comprises two main steps: the degree of support and confidence between the frequent item set and the calculated item set is identified. First, the algorithm identifies frequent item sets by iteratively considering item set combinations of different sizes. In each iteration, the algorithm calculates the frequency of occurrence of each set in the dataset and compares it with a preset minimum support threshold. Only those sets of items that meet the minimum support requirement are retained. For example, in analyzing motorcycle electrical system fault data, algorithms identify frequent sets of terms such as "high temperature" and "low battery voltage". The algorithm then calculates the support and confidence between these frequent item sets. Support refers to the frequency of occurrence of one item set in all data, while confidence is the conditional probability that in the case of occurrence of one item set, the other item set also occurs. These metrics help identify strong association rules. Finally, based on these frequent item sets and the support and confidence levels, the algorithm builds relationships between the item sets and generates a set of association rule candidates. In this process, a large number of candidate rules are generated, and therefore, further screening and optimization of these rules are required to ensure that the obtained rules have both statistical significance and practical application value.
In the rule evaluation sub-module, an evaluation work of the association rule candidate set is performed by carefully analyzing the support and confidence of the rule. In the evaluation process, the support and confidence of each rule are calculated first. The support reflects the popularity of the rule, i.e., the frequency with which the item sets in the rule appear in the entire dataset; the confidence level reflects the reliability of the rule, i.e. the conditional probability of the occurrence of the result item in case of the occurrence of the leading item. These metrics are key to measuring rule quality. Furthermore, evaluation of the rules also includes testing of the rules, which typically involves dividing the data set into a training set and a testing set to verify the performance of the rules on unknown data. This approach helps identify rules that are not only valid in the training data, but also have predictive value for the new data. In the final stage of rule evaluation, a final association rule set is screened out from the association rule candidate set based on the representativeness and accuracy of the rule. The screening process involves considering other metrics of the rule, such as Lift, which is a measure of how much additional information the rule provides. Only those rules that have a high degree of support, high confidence, and provide new insights will be selected into the final set of association rules. The generated association rule set provides a powerful tool for subsequent data analysis and decision-making. For example, in a motorcycle electrical system fault detection system, these rules may help identify which combinations of sensor readings are most likely to be predictive of an impending fault, thereby allowing maintenance personnel to take action in advance to prevent the occurrence of the fault.
It is assumed that the data set of the motorcycle electrical system includes data items of engine temperature, battery voltage, throttle position, etc., each having an analog value. During data preprocessing, outliers are identified and processed, such as replacing abnormally high engine temperature readings with average values. These data are then analyzed using the Apriori algorithm, and it is found that, for example, an increase in engine temperature is generally accompanied by a decrease in battery voltage. Through further rule evaluation and testing, the rule is finally determined to be used as a reliable association rule, and the reliable association rule is incorporated into an association rule set to be used as a basis for fault prediction and prevention.
Referring to fig. 9, the extreme condition analysis module includes a monte carlo simulation sub-module, a condition setting sub-module, and a stability and vulnerability assessment sub-module;
The Monte Carlo simulation submodule simulates system behavior and performance response by generating a batch of random samples based on an association rule set of the extreme condition analysis module and adopting a Monte Carlo simulation algorithm, so as to carry out probability analysis of system performance and response and generate an extreme working condition performance simulation result;
The working condition setting submodule adopts a decision tree analysis method based on the extreme working condition performance simulation result, establishes a decision model according to data in the result, classifies and carries out regression analysis on the system performance under various working conditions, and simultaneously determines key factors influencing the system performance to generate a refined working condition analysis result;
The stability and vulnerability assessment submodule builds a differential equation and a state space model reflecting the dynamic response of the system by adopting a dynamic simulation model of the system based on the refined working condition analysis result, assesses the stability and vulnerability of the system under various working conditions and generates extreme working condition response analysis.
In the Monte Carlo simulation sub-module, performance and response analysis under extreme working conditions is carried out on the motorcycle electrical system through a Monte Carlo simulation algorithm. Monte Carlo simulation is a statistical simulation method that simulates real situations by generating a large number of random samples, thereby evaluating the performance of the system under different conditions. This process first involves extracting relevant data from the association rule set of the extreme condition analysis module. For example, the data includes various states that the system may exhibit under certain conditions of temperature, voltage, etc. Using the monte carlo simulation algorithm, a series of random samples are first generated that reflect the state of the system under various conditions. This includes random combinations of various possible parameters for the electrical system, such as different temperatures, voltages and other operating conditions. In this way, the algorithm can simulate a broad, occurring set of scenes. Next, the algorithm analyzes these randomly generated samples to evaluate the performance and response of the system under various assumptions. This involves performing performance index calculations such as system stability, efficiency, and failure rate for each sample. Through analysis of a large number of samples, the algorithm can estimate the performance distribution of the system under different conditions and the fault risk. Finally, the simulation process generates an extreme condition performance simulation result. These results provide a thorough understanding of the behavior of the system under different terminal conditions, help identify weak points and direction of optimization, and provide support for developing more effective maintenance strategies and emergency measures.
In the working condition setting sub-module, based on the extreme working condition performance simulation result of Monte Carlo simulation, a decision tree analysis method is adopted to further analyze and classify the system performance. Decision trees are a popular machine learning method to classify or regression analyze data by creating a series of rules. In this sub-module, the decision tree is used to extract key information from the simulation results and build a decision model. First, the submodule extracts data from the Monte Carlo simulation results, where the data includes system performance indicators under different conditions. These data are then analyzed using a decision tree algorithm. This process involves selecting the appropriate characteristics (e.g., temperature, voltage, etc.), determining the split points, and building a tree structure in which each node represents a decision rule and each leaf node represents a predicted outcome. In this way, the decision tree model can effectively classify the system performance under various working conditions. At the same time, the process can identify key factors affecting system performance. For example, the model finds that at a certain voltage level, the system performance drops significantly, or the failure rate rises sharply over a certain temperature range. These findings are critical to understanding the impact of different operating conditions on system performance. Through analysis of the decision tree model, the generated refined working condition analysis result provides deep insight into the performance of the system under various different working conditions. These results not only help identify potential vulnerabilities of the system, but also provide important basis for optimizing design and developing preventive measures. For example, if poor system stability is found under certain conditions, certain enhancements may be made to these conditions to improve the overall robustness of the system.
In the stability and vulnerability assessment submodule, based on a refined working condition analysis result, a system dynamic simulation model is adopted to assess the stability and vulnerability of the system under various working conditions, and the process involves constructing a differential equation and a state space model which reflect the dynamic response of the system. Differential equations and state space models are key tools in dynamic system analysis, and can describe the change of system state over time, as well as the response of the system to external inputs (e.g., different operating conditions). This sub-module first defines the state variables of the system, such as voltage, temperature, etc., and then builds differential equations that express the dynamic changes of these state variables. For example, a differential equation may describe the manner in which battery voltage varies over time and usage conditions. The state space model links the system state, input (such as working condition change) and output (such as performance index), and provides a comprehensive framework for analyzing the response of the system under different working conditions. By modeling different input conditions, such as extreme temperature or voltage variations, this model can assess the stability and vulnerability of the system under these conditions. Such analysis reveals the behavior of the system under various conditions, helping to identify weak points that lead to performance degradation or failure occurrence under certain conditions. Finally, the extreme condition response analysis results generated by the stability and vulnerability assessment sub-module provide valuable guidance for the design and maintenance of the system. These results not only indicate the behavior of the system under certain conditions, but also reveal the dynamic nature of the system response, such as which conditions the system exhibits a high degree of stability and which conditions are susceptible to problems. This is important for preventing failures, optimizing system design, and improving overall reliability.
In the Monte Carlo simulation sub-module, it is assumed that the key data for the motorcycle electrical system includes engine temperature, battery voltage, throttle response time, etc. First, these data items are used to generate random samples based on the association rule set of the extreme condition analysis module. For example, monte Carlo simulation generates a random data combination of engine temperature at 80 to 120 degrees Celsius, battery voltage at 10 to 14 volts. These random samples were simulated to assess how the performance of the system varied under these extreme conditions. Simulation results show that the system failure rate increases significantly at high temperature and low voltage.
And in the working condition setting sub-module, analyzing data obtained by Monte Carlo simulation by using a decision tree analysis method. And constructing a decision tree model by taking the temperature and the voltage as characteristics to classify the system performance under different working conditions. Decision trees reveal that at high temperatures (> 100 degrees celsius) and low voltages (< 11 volts) the system fails. At the same time, this process reveals that temperature and voltage are key factors affecting system performance. The resulting operating mode analysis details the expected performance of the system at different parameter combinations.
In the stability and vulnerability assessment sub-module, the stability and vulnerability of the system under these specific conditions are assessed using a dynamic simulation model of the system based on the results of the condition setting sub-module. Differential equations and state space models are constructed that contain temperature and voltage changes. Simulations show that at temperatures exceeding 100 degrees celsius, the stability of the system drops rapidly, while at voltages below 11 volts, the response time of the system increases, increasing the risk of failure. These analyses help identify the vulnerability of the system to certain conditions, providing a basis for future design improvements and preventative maintenance.
Referring to fig. 10, the dynamic threshold adjustment module includes an adaptive algorithm implementation sub-module, a threshold calculation sub-module, and an environment monitoring feedback sub-module;
the self-adaptive algorithm realizes the response analysis of the submodule based on the extreme working condition, adopts a self-adaptive control algorithm, adjusts algorithm parameters by analyzing the system state and the prediction data in real time, continuously monitors the system state, estimates future behaviors by using a prediction model, and generates a self-adaptive parameter adjustment scheme according to the prediction adjustment control parameters;
The threshold calculation sub-module is based on a self-adaptive parameter adjustment scheme, adopts a dynamic threshold calculation method, combines real-time monitoring data and historical performance indexes, determines an optimal threshold by analyzing the statistical characteristics and the change trend of the data, comprises the analysis of the centralized trend and the distribution characteristic of the data, and generates an optimized threshold parameter based on the continuous optimization of the analysis result on the fault detection threshold;
The environment monitoring feedback submodule continuously monitors external environment changes and dynamically adjusts the threshold according to the changes by adopting an environment sensing technology and a self-adaptive adjustment strategy based on the optimized threshold parameters, simultaneously collects environment data by utilizing a sensor network, dynamically adjusts the fault detection threshold according to the collected data, matches the differential working condition and generates adjusted threshold parameters.
In the self-adaptive algorithm realization sub-module, the self-adaptive control algorithm is used for dynamically managing and optimizing the motorcycle electrical system. The core of the adaptive algorithm is that it is able to dynamically adjust its parameters based on real-time data and environmental changes to optimize system performance and response. The data formats employed by this process typically include system real-time status data (e.g., voltage, temperature, current, etc.) and predictive data (e.g., predicted temperature changes or voltage fluctuations). The adaptive algorithm first analyzes the current state of the system in real time, which includes collecting sensor data, evaluating system performance metrics, and monitoring any significant changes or trends. The algorithm then uses this information to adjust its operating parameters, such as modifying the control logic or adjusting the fault detection threshold. For example, if a sustained decrease in battery voltage is detected, the algorithm increases the frequency of monitoring the voltage, or adjusts a performance evaluation parameter associated with the voltage. Meanwhile, the adaptive algorithm uses a predictive model to estimate the future behavior of the system. This typically involves machine learning techniques, such as time series analysis or regression models, for predicting future states based on historical data. Based on these predictions, the algorithm further adjusts the control parameters to address changes or faults in advance. The generated self-adaptive parameter adjustment scheme enables the system to more flexibly cope with various running conditions and environmental changes, and improves the reliability and efficiency of the system. For example, the approach includes enhancing a control strategy of the cooling system under high temperature conditions, or adjusting a power management strategy when the voltage is unstable.
In the threshold calculation sub-module, a dynamic threshold calculation method is adopted to continuously optimize the fault detection threshold based on the self-adaptive parameter adjustment scheme. The process combines the real-time monitoring data and the historical performance indexes, and determines the optimal threshold value by analyzing the statistical characteristics and the change trend of the data. For example, by analyzing battery voltage data over a period of time, a normal range of voltages can be determined and a threshold for fault detection set accordingly. Dynamic threshold calculation not only focuses on the central tendency (such as average value or median) of data, but also considers the distribution characteristics (such as variance or bias) of data. Thus, the actual running condition of the system can be reflected more accurately, and the threshold value can be adjusted in time to adapt to the change of the system. The generated optimized threshold parameters are continually updated to match the actual performance and operating conditions of the system. For example, if engine temperature generally increases in a summer high temperature environment, the threshold calculation sub-module may adjust the temperature-dependent fault detection threshold to avoid frequent false positives.
In the environment monitoring feedback sub-module, based on the optimized threshold parameter, an environment sensing technology and an adaptive adjustment strategy are adopted to continuously monitor external environment changes and correspondingly adjust the fault detection threshold. In the process, the sensor network collects environmental and system operation data such as temperature, humidity, vibration intensity and the like at each key position of the motorcycle. The format of this data, including real-time readings and historical data, is used to monitor any significant changes in the environment and system status. The environmental monitoring feedback submodule uses this data to dynamically adjust the fault detection threshold to match changing operating conditions. For example, if the sensor data shows a significant increase in ambient temperature, the sub-module may automatically adjust the temperature-dependent threshold to reflect this change. The dynamic adjustment ensures that the fault detection mechanism is sensitive and accurate, and false alarms or missing alarms caused by environmental changes are prevented. In addition, the submodule analyzes the collected data to optimize the adaptive capacity of the system. By analyzing long-term trends and patterns of environmental data, the sub-modules can further refine their adjustment strategies to predict and cope with environmental changes. The generated adjusted threshold parameters not only improve the accuracy of fault detection, but also enhance the adaptability of the system to environmental changes. Considering that both the external temperature and the system heating value are high during riding in summer, the environment monitoring feedback sub-module can automatically adjust the temperature related threshold value to adapt to the seasonal change. Also, if an increase in humidity is detected, the threshold of the electrical connection may be adjusted to prevent humidity-induced shorting or corrosion problems.
In the adaptive algorithm implementation sub-module, it is assumed that the key data of the motorcycle electrical system includes engine temperature, battery voltage, throttle response time. In a high temperature environment, the system monitors the continuous rising trend of the temperature of the engine in real time. The adaptive algorithm adjusts its parameters in real time according to the trend, for example, to improve the working efficiency of the heat dissipation system, and simultaneously predicts the trend of temperature change in a future period of time. If the prediction indicates that the temperature will continue to rise, the algorithm further adjusts the cooling strategy to prevent overheating faults.
In the threshold calculation sub-module, based on the data and predictions generated by the adaptive algorithm, this module dynamically calculates the optimal threshold for fault detection. For example, by analyzing historical data and current environmental conditions, it is found that the normal operating temperature range of the engine should be up-regulated at high temperatures. Therefore, the threshold value of the overheat fault is adjusted from the original 95 ℃ to 100 ℃ so as to reduce false alarm and ensure that the system can safely operate at a higher temperature.
In the environment monitoring feedback sub-module, the module continuously monitors environmental changes such as temperature, humidity, vibration and the like by utilizing a sensor network arranged on the motorcycle during the running process of the motorcycle. For example, when the motorcycle enters a region of higher humidity, the module detects a change in ambient humidity and dynamically adjusts the humidity threshold of the electrical system to prevent circuit problems due to humidity.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.