技术领域Technical field
本发明涉及故障诊断技术领域，尤其涉及一种HPLC故障诊断设备的检测系统及方法。The present invention relates to the technical field of fault diagnosis, and in particular to a detection system and method for HPLC fault diagnosis equipment.
背景技术Background technique
故障诊断技术领域专注于高效液相色谱设备的性能维持、故障预防和故障修复，在现代实验室和工业分析中，HPLC是一种广泛使用的技术，用于分离、识别和定量混合物中的各种成分，由于其在药物分析、生物化学、环境测试等领域的关键作用，HPLC设备的维护和故障诊断成为确保数据准确性和实验效率的重要环节，因此，这一技术领域不仅关注设备的物理维护，还包括软件诊断、数据分析和预测维护策略的开发。The field of fault diagnosis technology focuses on the performance maintenance, fault prevention and fault repair of high-performance liquid chromatography equipment. In modern laboratories and industrial analysis, HPLC is a widely used technology for separating, identifying and quantifying various components in mixtures. Due to its key role in pharmaceutical analysis, biochemistry, environmental testing and other fields, the maintenance and fault diagnosis of HPLC equipment have become important links to ensure data accuracy and experimental efficiency. Therefore, this technical field not only focuses on the physical properties of the equipment Maintenance, also includes software diagnostics, data analysis and the development of predictive maintenance strategies.
其中，HPLC故障诊断设备的检测系统是专门设计用于识别和分析HPLC设备潜在或现有故障的系统，其主要目的是通过及时识别和解决这些故障，保证HPLC设备的正常运行和数据的准确性，系统包括硬件传感器和软件分析工具，能够检测设备的物理状态（如压力、温度、流量等）和操作参数（如分离效率、峰形、检测器响应等），从而提供关于设备健康状况的实时反馈，通过这种方式，该系统旨在减少设备故障带来的停机时间，提高实验室工作效率，同时确保实验结果的可靠性，同时，系统通过一系列高精度传感器、数据采集单元和先进的分析软件实现其功能，传感器用于实时监测HPLC系统的关键性能指标，如流动相的压力、温度、流速等，数据采集单元则收集这些传感器的输出数据，并将其传输给分析软件，分析软件是这一系统的核心，它利用先进的算法，如机器学习和模式识别，对收集到的数据进行深入分析，以识别设备的任何异常行为或潜在故障，此外，这些软件还可能包括故障诊断工具和维护方案，帮助用户快速定位问题原因并采取相应的维护措施，通过这种综合的硬件和软件解决方案，能够有效地实现对HPLC设备故障的预测、诊断和解决。Among them, the detection system of HPLC fault diagnosis equipment is a system specially designed to identify and analyze potential or existing faults of HPLC equipment. Its main purpose is to ensure the normal operation of HPLC equipment and the accuracy of data by promptly identifying and solving these faults. , the system includes hardware sensors and software analysis tools that can detect the physical status of the device (such as pressure, temperature, flow, etc.) and operating parameters (such as separation efficiency, peak shape, detector response, etc.), thereby providing real-time information on the health of the device. Feedback, in this way, the system aims to reduce downtime caused by equipment failure, improve laboratory work efficiency, and ensure the reliability of experimental results. At the same time, the system uses a series of high-precision sensors, data acquisition units and advanced The analysis software realizes its functions. The sensors are used to monitor the key performance indicators of the HPLC system in real time, such as the pressure, temperature, flow rate of the mobile phase, etc. The data acquisition unit collects the output data of these sensors and transmits it to the analysis software. The analysis software At the core of this system, it uses advanced algorithms such as machine learning and pattern recognition to conduct in-depth analysis of the collected data to identify any abnormal behavior or potential failures of the equipment. In addition, these software may also include fault diagnosis tools. and maintenance plans to help users quickly locate the cause of problems and take corresponding maintenance measures. Through this comprehensive hardware and software solution, HPLC equipment faults can be effectively predicted, diagnosed and resolved.
尽管现有的HPLC故障诊断设备检测系统已经具备高效的功能，如高精度传感器和先进的分析软件，确保了设备的正常运行和数据的准确性，但在面对复杂、多变的故障诊断需求时，这些系统仍显示出一些局限性，特别是在数据处理方法的多样性和深度方面，传统的机器学习和模式识别算法难以充分处理复杂或非线性的高维数据，从而影响故障预测和分类的准确度，此外，现有系统在动态适应不同设备运行阶段的能力方面也存在不足，如对设备启动、稳态运行和关闭阶段的故障检测策略缺乏足够的灵活性和针对性，在故障诊断的深度和细化程度方面，现有系统未能达到追踪复杂故障根源和进行多层次故障分析的需求，同时，故障模式的识别和分类也需要更加精细化和深入的方法处理多样化和复杂的故障模式，最后，现有系统在提供全面和优化的故障处理方案方面，还有进一步完善和发展的空间，综上所述，虽然现有系统在某些方面表现出色，但在数据处理的先进性、故障诊断的深度与细化、动态适应能力以及决策支持系统的完善性方面，仍有显著的提升空间。Although the existing HPLC fault diagnosis equipment detection system already has efficient functions, such as high-precision sensors and advanced analysis software, ensuring the normal operation of the equipment and the accuracy of data, in the face of complex and changing fault diagnosis needs However, these systems still show some limitations, especially in terms of the diversity and depth of data processing methods. Traditional machine learning and pattern recognition algorithms are difficult to adequately handle complex or nonlinear high-dimensional data, thus affecting fault prediction and classification. The accuracy of In terms of depth and refinement, existing systems fail to meet the needs of tracking the root causes of complex faults and conducting multi-level fault analysis. At the same time, the identification and classification of fault modes also require more refined and in-depth methods to handle diverse and complex problems. Failure modes. Finally, the existing system still has room for further improvement and development in terms of providing comprehensive and optimized fault handling solutions. In summary, although the existing system performs well in some aspects, it is not advanced in data processing. There is still significant room for improvement in terms of performance, depth and refinement of fault diagnosis, dynamic adaptability, and completeness of the decision support system.
发明内容Contents of the invention
本发明的目的是解决现有技术中存在的缺点，而提出的一种HPLC故障诊断设备的检测系统及方法。The purpose of the present invention is to solve the shortcomings existing in the prior art and propose a detection system and method for HPLC fault diagnosis equipment.
为了实现上述目的，本发明采用了如下技术方案：一种HPLC故障诊断设备的检测系统包括逻辑回归分析模块、波动监控模块、多阶段管理模块、递归诊断模块、流形学习模块、故障模式识别模块、综合决策模块；In order to achieve the above objectives, the present invention adopts the following technical solution: a detection system for HPLC fault diagnosis equipment including a logistic regression analysis module, a fluctuation monitoring module, a multi-stage management module, a recursive diagnosis module, a manifold learning module, and a fault mode identification module. , comprehensive decision-making module;
所述逻辑回归分析模块基于HPLC设备的运行数据，采用逻辑回归算法进行故障模式分析，结合贝叶斯优化调整模型的预测能力，生成故障类型预测结果；The logistic regression analysis module uses the logistic regression algorithm to perform failure mode analysis based on the operating data of the HPLC equipment, and combines the prediction capabilities of the Bayesian optimization adjustment model to generate fault type prediction results;
所述波动监控模块基于故障类型预测结果，采用自回归移动平均模型和卡尔曼滤波器进行设备性能参数的波动分析，并发现即时异常情况，生成波动分析报告；The fluctuation monitoring module uses the autoregressive moving average model and the Kalman filter to perform fluctuation analysis of equipment performance parameters based on the fault type prediction results, discovers immediate abnormal conditions, and generates a fluctuation analysis report;
所述多阶段管理模块基于波动分析报告，采用模糊逻辑控制和隐马尔可夫模型，根据设备运行阶段自动调整检测参数，生成阶段性故障诊断结果；The multi-stage management module is based on the fluctuation analysis report, uses fuzzy logic control and hidden Markov model, automatically adjusts detection parameters according to the equipment operating stage, and generates staged fault diagnosis results;
所述递归诊断模块基于阶段性故障诊断结果，采用递归神经网络和决策树进行迭代诊断，并分析故障原因，生成迭代诊断报告；The recursive diagnosis module uses recursive neural networks and decision trees to perform iterative diagnosis based on staged fault diagnosis results, analyzes the cause of the fault, and generates an iterative diagnosis report;
所述流形学习模块基于迭代诊断报告，采用局部线性嵌入和等度量映射分析故障模式，揭示数据内在结构，生成故障模式识别结果；Based on the iterative diagnosis report, the manifold learning module uses local linear embedding and equimetric mapping to analyze fault modes, reveal the intrinsic structure of the data, and generate fault mode identification results;
所述故障模式识别模块基于故障模式识别结果，采用支持向量机和K-最近邻算法，进行故障模式的细化和分类，并为故障处理提供指导，生成细化的故障模式分析报告；The fault mode recognition module uses support vector machines and K-nearest neighbor algorithms to refine and classify fault modes based on the fault mode recognition results, provides guidance for fault handling, and generates a detailed fault mode analysis report;
所述综合决策模块基于细化的故障模式分析报告，综合逻辑回归分析模块、波动监控模块、多阶段管理模块、递归诊断模块、流形学习模块的输出结果，采用知识图谱辅助的决策分析和决策树分析，进行最终诊断和处理方案，生成综合诊断决策。The comprehensive decision-making module is based on the detailed failure mode analysis report, comprehensive output results of the logistic regression analysis module, fluctuation monitoring module, multi-stage management module, recursive diagnosis module, and manifold learning module, and uses knowledge graph-assisted decision analysis and decision-making Tree analysis is used to perform final diagnosis and treatment plans, and generate comprehensive diagnostic decisions.
作为本发明的进一步方案，所述故障类型预测结果具体为故障种类的概率分布，所述波动分析报告包括关键参数的波动趋势、异常模式，所述阶段性故障诊断结果具体指多运行阶段的故障特征和状态，所述迭代诊断报告包括故障原因的层级分析、层级描述，所述故障模式识别结果具体为多故障类型的数据模式和分类，所述细化的故障模式分析报告具体指多故障模式的特征和分类，所述综合诊断决策包括故障处理的综合方案、优化策略。As a further solution of the present invention, the fault type prediction results are specifically the probability distribution of fault types, the fluctuation analysis report includes the fluctuation trends and abnormal patterns of key parameters, and the phased fault diagnosis results specifically refer to faults in multiple operating stages. Characteristics and status, the iterative diagnosis report includes hierarchical analysis and hierarchical description of fault causes, the fault mode identification results are specifically data patterns and classifications of multiple fault types, and the detailed fault mode analysis report specifically refers to multiple fault modes Characteristics and classifications, the comprehensive diagnosis decision-making includes comprehensive solutions and optimization strategies for fault handling.
作为本发明的进一步方案，所述逻辑回归分析模块包括模型训练子模块、预测分析子模块、模型优化子模块；As a further solution of the present invention, the logistic regression analysis module includes a model training sub-module, a predictive analysis sub-module, and a model optimization sub-module;
所述模型训练子模块基于HPLC设备的运行数据，采用罗吉斯回归模型，并结合权重调整技术，对故障模式进行初步分析和模型建立，生成初步故障模式模型；The model training sub-module is based on the operating data of HPLC equipment, uses Logis regression model, and combines weight adjustment technology to conduct preliminary analysis and model establishment of the failure mode, and generates a preliminary failure mode model;
所述预测分析子模块基于初步故障模式模型，采用高斯分布分析，再结合条件概率方法，进行故障类型的概率预测分析，生成故障类型概率分析结果；The prediction analysis sub-module is based on the preliminary failure mode model, uses Gaussian distribution analysis, and combines with the conditional probability method to perform probabilistic prediction analysis of fault types and generate failure type probability analysis results;
所述模型优化子模块基于故障类型概率分析结果，采用贝叶斯网络和梯度提升算法，调整模型参数，并优化预测性能，生成故障类型预测结果。Based on the fault type probability analysis results, the model optimization sub-module uses Bayesian network and gradient boosting algorithm to adjust model parameters, optimize prediction performance, and generate fault type prediction results.
作为本发明的进一步方案，所述波动监控模块包括实时监控子模块、波动分析子模块、异常检测子模块；As a further solution of the present invention, the fluctuation monitoring module includes a real-time monitoring sub-module, a fluctuation analysis sub-module, and an anomaly detection sub-module;
所述实时监控子模块基于故障类型预测结果，采用流数据处理技术，对设备性能参数进行实时监控，生成实时性能数据报告；The real-time monitoring sub-module uses streaming data processing technology to monitor equipment performance parameters in real time based on fault type prediction results and generates real-time performance data reports;
所述波动分析子模块基于实时性能数据报告，采用自回归积分滑动平均模型，对设备参数波动进行分析，生成波动趋势分析报告；The fluctuation analysis sub-module is based on real-time performance data reports and uses an autoregressive integral moving average model to analyze equipment parameter fluctuations and generate a fluctuation trend analysis report;
所述异常检测子模块基于波动趋势分析报告，采用多元卡尔曼滤波技术，识别并定位异常波动，生成波动分析报告。The anomaly detection sub-module is based on the fluctuation trend analysis report and uses multivariate Kalman filtering technology to identify and locate abnormal fluctuations and generate a fluctuation analysis report.
作为本发明的进一步方案，所述多阶段管理模块包括初始化检测子模块、稳态运行子模块、运行调整子模块；As a further solution of the present invention, the multi-stage management module includes an initialization detection sub-module, a steady-state operation sub-module, and an operation adjustment sub-module;
所述初始化检测子模块基于波动分析报告，采用主成分分析，评估设备启动时的性能状况，生成启动阶段诊断结果；The initialization detection sub-module is based on the fluctuation analysis report and uses principal component analysis to evaluate the performance of the equipment when it is started, and generate startup phase diagnostic results;
所述稳态运行子模块基于启动阶段诊断结果，采用时间序列分析和趋势监测技术，评估设备运行期间的表现，生成稳态运行分析报告；The steady-state operation sub-module uses time series analysis and trend monitoring technology to evaluate the performance of the equipment during operation based on the diagnosis results during the startup phase, and generates a steady-state operation analysis report;
所述运行调整子模块基于稳态运行分析报告，采用模糊逻辑控制器和动态条件随机场算法，自动调整检测参数，并适应多种运行状态，生成阶段性故障诊断结果。The operation adjustment sub-module is based on the steady-state operation analysis report, uses fuzzy logic controller and dynamic condition random field algorithm, automatically adjusts detection parameters, adapts to multiple operating states, and generates phased fault diagnosis results.
作为本发明的进一步方案，所述递归诊断模块包括初步诊断子模块、迭代分析子模块、决策树导向子模块；As a further solution of the present invention, the recursive diagnosis module includes a preliminary diagnosis sub-module, an iterative analysis sub-module, and a decision tree guidance sub-module;
所述初步诊断子模块基于阶段性故障诊断结果，采用深度递归神经网络，对故障进行初始分析，并进行数据融合，生成初步故障分析报告；The preliminary diagnosis sub-module uses a deep recursive neural network to conduct initial analysis of the fault based on the staged fault diagnosis results, conducts data fusion, and generates a preliminary fault analysis report;
所述迭代分析子模块基于初步故障分析报告，采用长短期记忆网络和多层感知机进行迭代分析，并探索故障的原因，生成故障原因分析报告；The iterative analysis sub-module uses the long short-term memory network and multi-layer perceptron to perform iterative analysis based on the preliminary fault analysis report, explores the cause of the fault, and generates a fault cause analysis report;
所述决策树导向子模块基于故障原因分析报告，采用随机森林对故障原因进行结构化排序和分类，生成迭代诊断报告。The decision tree guidance sub-module uses random forest to structurally sort and classify the fault causes based on the fault cause analysis report, and generates an iterative diagnosis report.
作为本发明的进一步方案，所述流形学习模块包括数据降维子模块、模式分析子模块、故障分类子模块；As a further solution of the present invention, the manifold learning module includes a data dimensionality reduction sub-module, a pattern analysis sub-module, and a fault classification sub-module;
所述数据降维子模块基于迭代诊断报告，采用局部线性嵌入技术，对故障数据进行降维处理，并突显关键特征，生成降维后的故障数据；The data dimensionality reduction sub-module is based on the iterative diagnosis report and uses local linear embedding technology to perform dimensionality reduction processing on the fault data, highlights key features, and generates dimensionally reduced fault data;
所述模式分析子模块基于降维后的故障数据，采用等度量映射技术，探究故障模式的内在联系，生成故障模式分析报告；Based on the dimensionally reduced fault data, the mode analysis sub-module uses equal metric mapping technology to explore the internal connections of the fault modes and generate a fault mode analysis report;
所述故障分类子模块基于故障模式分析报告，采用谱聚类算法，对故障模式进行分类，生成故障模式识别结果。The fault classification sub-module uses a spectral clustering algorithm to classify fault modes based on the fault mode analysis report and generate fault mode identification results.
作为本发明的进一步方案，所述故障模式识别模块包括模式对比子模块、模式验证子模块、结果细化子模块；As a further solution of the present invention, the fault mode identification module includes a mode comparison sub-module, a mode verification sub-module, and a result refinement sub-module;
所述模式对比子模块基于故障模式识别结果，采用模式匹配技术，对比分析多类故障模式之间的相似性和差异性，生成模式对比分析结果；The mode comparison sub-module uses pattern matching technology to comparatively analyze the similarities and differences between multiple types of fault modes based on the fault mode recognition results, and generates mode comparison analysis results;
所述模式验证子模块基于模式对比分析结果，采用交叉验证和统计验证技术，对故障模式的可靠性进行确认，生成模式验证报告；The mode verification sub-module uses cross-validation and statistical verification techniques to confirm the reliability of the failure mode based on the mode comparison analysis results and generates a mode verification report;
所述结果细化子模块基于模式验证报告，采用支持向量机和K-最近邻算法，对故障模式进行分析和分类调整，生成细化的故障模式分析报告。The result refinement sub-module is based on the pattern verification report and uses support vector machine and K-nearest neighbor algorithm to analyze, classify and adjust the failure mode and generate a refined failure mode analysis report.
作为本发明的进一步方案，所述综合决策模块包括结果汇总子模块、决策制定子模块、优化策略子模块；As a further solution of the present invention, the comprehensive decision-making module includes a result summary sub-module, a decision-making sub-module, and an optimization strategy sub-module;
所述结果汇总子模块基于细化的故障模式分析报告，综合逻辑回归分析模块、波动监控模块、多阶段管理模块、递归诊断模块、流形学习模块的输出结果，采用数据聚合技术和主成分分析，进行数据归纳汇总，生成综合数据报告；The result summary sub-module is based on a detailed failure mode analysis report, comprehensive output results of the logistic regression analysis module, fluctuation monitoring module, multi-stage management module, recursive diagnosis module, and manifold learning module, and adopts data aggregation technology and principal component analysis. , conduct data summary and generate comprehensive data reports;
所述决策制定子模块基于综合数据报告，采用知识图谱辅助的决策分析和Gini指数分析，对综合数据进行解析，并提出故障诊断和处理方案，生成初步决策方案；The decision-making sub-module is based on the comprehensive data report, uses knowledge graph-assisted decision analysis and Gini index analysis to analyze the comprehensive data, proposes fault diagnosis and processing plans, and generates preliminary decision-making plans;
所述优化策略子模块基于初步决策方案，采用模拟退火优化算法和场景分析技术，细化和优化决策方案内容，生成综合诊断决策。The optimization strategy sub-module is based on the preliminary decision-making plan and uses simulated annealing optimization algorithm and scene analysis technology to refine and optimize the content of the decision-making plan and generate comprehensive diagnostic decisions.
一种HPLC故障诊断设备的检测方法，所述HPLC故障诊断设备的检测方法基于上述HPLC故障诊断设备的检测系统执行，包括以下步骤：A detection method of HPLC fault diagnosis equipment. The detection method of HPLC fault diagnosis equipment is executed based on the detection system of the above-mentioned HPLC fault diagnosis equipment and includes the following steps:
S1：基于HPLC设备的运行数据，采用逻辑回归算法，结合贝叶斯优化技术对故障模式进行分析，并进行模式的识别，生成故障类型预测结果；S1: Based on the operating data of HPLC equipment, use logistic regression algorithm and Bayesian optimization technology to analyze the fault mode, identify the mode, and generate fault type prediction results;
S2：基于所述故障类型预测结果，采用自回归移动平均模型和卡尔曼滤波器，进行设备性能参数的波动分析，识别并记录异常情况，生成波动分析报告；S2: Based on the fault type prediction results, use the autoregressive moving average model and Kalman filter to conduct fluctuation analysis of equipment performance parameters, identify and record abnormal conditions, and generate a fluctuation analysis report;
S3：基于所述波动分析报告，采用模糊逻辑控制和隐马尔可夫模型，根据设备运行阶段自动调整检测参数，生成阶段性故障诊断结果；S3: Based on the fluctuation analysis report, fuzzy logic control and hidden Markov model are used to automatically adjust detection parameters according to the equipment operating stage and generate phased fault diagnosis results;
S4：基于所述阶段性故障诊断结果，采用递归神经网络，结合决策树分析对故障进行迭代诊断，并探究故障原因，生成迭代诊断报告；S4: Based on the staged fault diagnosis results, use a recursive neural network and decision tree analysis to iteratively diagnose the fault, explore the cause of the fault, and generate an iterative diagnosis report;
S5：基于所述迭代诊断报告，采用局部线性嵌入和等度量映射技术，分析故障模式，并揭示数据的内在结构，生成故障模式识别结果；S5: Based on the iterative diagnosis report, use local linear embedding and equimetric mapping technology to analyze the fault mode, reveal the intrinsic structure of the data, and generate fault mode identification results;
S6：基于所述故障模式识别结果，采用支持向量机和K-最近邻算法，对故障模式进行细化分析和分类，并为故障处理提供指导，生成细化的故障模式分析报告；S6: Based on the fault mode identification results, use support vector machine and K-nearest neighbor algorithm to conduct detailed analysis and classification of the fault mode, provide guidance for fault handling, and generate a detailed fault mode analysis report;
S7：基于所述细化的故障模式分析报告，综合故障模式识别结果、迭代诊断报告、阶段性故障诊断结果、波动分析报告、故障类型预测结果的内容，采用知识图谱辅助的决策分析和Gini指数分析，提出故障诊断和处理方案，生成初步决策方案；S7: Based on the detailed fault mode analysis report, comprehensively integrate the contents of the fault mode identification results, iterative diagnosis reports, staged fault diagnosis results, fluctuation analysis reports, and fault type prediction results, and use knowledge graph-assisted decision analysis and Gini index Analyze, propose fault diagnosis and treatment plans, and generate preliminary decision-making plans;
S8：基于所述初步决策方案，采用模拟退火优化算法和场景分析技术，对决策方案进行细化和优化，生成综合诊断决策。S8: Based on the preliminary decision-making plan, use simulated annealing optimization algorithm and scene analysis technology to refine and optimize the decision-making plan and generate a comprehensive diagnostic decision.
与现有技术相比，本发明的优点和积极效果在于：Compared with the existing technology, the advantages and positive effects of the present invention are:
本发明中，通过整合逻辑回归与贝叶斯优化、自回归移动平均模型及卡尔曼滤波器等数据处理技术，显著增强了对复杂、非线性高维数据的处理能力，这种多元化和深层次的分析方法在故障预测和分类的精确度上远超传统机器学习和模式识别算法，其次，引入的多阶段管理模块采用模糊逻辑控制和隐马尔可夫模型，能够有效应对设备不同运行阶段的故障检测需求，显著提升了系统的动态适应能力和故障检测的针对性，在故障诊断的深度和细化方面，递归诊断模块结合递归神经网络和决策树技术，为追踪复杂故障源和进行多层次分析提供了强大的工具，超越了现有系统的局限，此外，引入的流形学习模块以其对复杂故障模式的精细化识别和分类，提供了对多样化故障模式的深入分析，弥补了现有系统在细致分析上的不足，最后，综合决策模块，特别是结合知识图谱辅助的决策分析，为故障处理提供了全面和优化的策略，极大地提高了决策支持系统的完善性和实用性。In the present invention, by integrating logistic regression, Bayesian optimization, autoregressive moving average model, Kalman filter and other data processing technologies, the processing capability of complex, nonlinear high-dimensional data is significantly enhanced. This diversified and deep The hierarchical analysis method far exceeds traditional machine learning and pattern recognition algorithms in the accuracy of fault prediction and classification. Secondly, the introduced multi-stage management module uses fuzzy logic control and hidden Markov model, which can effectively cope with the problems of different operating stages of the equipment. Fault detection requirements have significantly improved the dynamic adaptability of the system and the pertinence of fault detection. In terms of depth and refinement of fault diagnosis, the recursive diagnosis module combines recursive neural networks and decision tree technology to track complex fault sources and conduct multi-level The analysis provides powerful tools that go beyond the limitations of existing systems. In addition, the introduced manifold learning module provides in-depth analysis of diverse failure modes with its refined identification and classification of complex failure modes, making up for the existing problems. There are deficiencies in detailed analysis of the system. Finally, the comprehensive decision-making module, especially the decision-making analysis assisted by the knowledge graph, provides a comprehensive and optimized strategy for fault handling, which greatly improves the completeness and practicality of the decision-making support system.
附图说明Description of the drawings
图1为本发明的系统流程图；Figure 1 is a system flow chart of the present invention;
图2为本发明的系统框架示意图；Figure 2 is a schematic diagram of the system framework of the present invention;
图3为本发明逻辑回归分析模块的流程图；Figure 3 is a flow chart of the logistic regression analysis module of the present invention;
图4为本发明波动监控模块的流程图；Figure 4 is a flow chart of the fluctuation monitoring module of the present invention;
图5为本发明多阶段管理模块的流程图；Figure 5 is a flow chart of the multi-stage management module of the present invention;
图6为本发明递归诊断模块的流程图；Figure 6 is a flow chart of the recursive diagnosis module of the present invention;
图7为本发明流形学习模块的流程图；Figure 7 is a flow chart of the manifold learning module of the present invention;
图8为本发明故障模式识别模块的流程图；Figure 8 is a flow chart of the fault mode identification module of the present invention;
图9为本发明综合决策模块的流程图；Figure 9 is a flow chart of the comprehensive decision-making module of the present invention;
图10为本发明的方法步骤示意图。Figure 10 is a schematic diagram of the method steps of the present invention.
具体实施方式Detailed ways
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.
在本发明的描述中，需要理解的是，术语“长度”、“宽度”、“上”、“下”、“前”、“后”、“左”、“右”、“竖直”、“水平”、“顶”、“底”、“内”、“外”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。此外，在本发明的描述中，“多个”的含义是两个或两个以上，除非另有明确具体的限定。In the description of the present invention, it should be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", The orientations or positional relationships indicated by "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description. It is not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore is not to be construed as a limitation of the invention. In addition, in the description of the present invention, "plurality" means two or more than two, unless otherwise clearly and specifically limited.
实施例一：请参阅图1-2，本发明提供一种技术方案：一种HPLC故障诊断设备的检测系统包括逻辑回归分析模块、波动监控模块、多阶段管理模块、递归诊断模块、流形学习模块、故障模式识别模块、综合决策模块；Embodiment 1: Please refer to Figures 1-2. The present invention provides a technical solution: a detection system for HPLC fault diagnosis equipment including a logistic regression analysis module, a fluctuation monitoring module, a multi-stage management module, a recursive diagnosis module, and a manifold learning module. module, fault mode identification module, comprehensive decision-making module;
逻辑回归分析模块基于HPLC设备的运行数据，采用逻辑回归算法进行故障模式分析，结合贝叶斯优化调整模型的预测能力，生成故障类型预测结果；The logistic regression analysis module uses the logistic regression algorithm to analyze failure modes based on the operating data of HPLC equipment, and combines the prediction capabilities of the Bayesian optimization adjustment model to generate fault type prediction results;
波动监控模块基于故障类型预测结果，采用自回归移动平均模型和卡尔曼滤波器进行设备性能参数的波动分析，并发现即时异常情况，生成波动分析报告；Based on the fault type prediction results, the fluctuation monitoring module uses the autoregressive moving average model and Kalman filter to conduct fluctuation analysis of equipment performance parameters, discovers immediate anomalies, and generates a fluctuation analysis report;
多阶段管理模块基于波动分析报告，采用模糊逻辑控制和隐马尔可夫模型，根据设备运行阶段自动调整检测参数，生成阶段性故障诊断结果；Based on the fluctuation analysis report, the multi-stage management module uses fuzzy logic control and hidden Markov model to automatically adjust detection parameters according to the equipment operating stage and generate staged fault diagnosis results;
递归诊断模块基于阶段性故障诊断结果，采用递归神经网络和决策树进行迭代诊断，并分析故障原因，生成迭代诊断报告；The recursive diagnosis module uses recursive neural networks and decision trees to perform iterative diagnosis based on staged fault diagnosis results, analyzes the cause of the fault, and generates an iterative diagnosis report;
流形学习模块基于迭代诊断报告，采用局部线性嵌入和等度量映射分析故障模式，揭示数据内在结构，生成故障模式识别结果；Based on the iterative diagnosis report, the manifold learning module uses local linear embedding and equimetric mapping to analyze fault modes, reveal the intrinsic structure of the data, and generate fault mode identification results;
故障模式识别模块基于故障模式识别结果，采用支持向量机和K-最近邻算法，进行故障模式的细化和分类，并为故障处理提供指导，生成细化的故障模式分析报告；Based on the fault mode recognition results, the fault mode identification module uses support vector machines and K-nearest neighbor algorithms to refine and classify fault modes, provide guidance for fault handling, and generate detailed fault mode analysis reports;
综合决策模块基于细化的故障模式分析报告，综合逻辑回归分析模块、波动监控模块、多阶段管理模块、递归诊断模块、流形学习模块的输出结果，采用知识图谱辅助的决策分析和决策树分析，进行最终诊断和处理方案，生成综合诊断决策。The comprehensive decision-making module is based on the detailed failure mode analysis report, comprehensively integrates the output results of the logistic regression analysis module, fluctuation monitoring module, multi-stage management module, recursive diagnosis module, and manifold learning module, and uses knowledge graph-assisted decision analysis and decision tree analysis. , make final diagnosis and treatment plan, and generate comprehensive diagnostic decisions.
故障类型预测结果具体为故障种类的概率分布，波动分析报告包括关键参数的波动趋势、异常模式，阶段性故障诊断结果具体指多运行阶段的故障特征和状态，迭代诊断报告包括故障原因的层级分析、层级描述，故障模式识别结果具体为多故障类型的数据模式和分类，细化的故障模式分析报告具体指多故障模式的特征和分类，综合诊断决策包括故障处理的综合方案、优化策略。The fault type prediction results specifically refer to the probability distribution of fault types. The fluctuation analysis report includes the fluctuation trends and abnormal patterns of key parameters. The staged fault diagnosis results specifically refer to the fault characteristics and status of multiple operating stages. The iterative diagnosis report includes hierarchical analysis of fault causes. , Hierarchical description, the failure mode identification results specifically refer to the data patterns and classifications of multiple fault types, the detailed failure mode analysis report specifically refers to the characteristics and classification of multiple fault modes, and the comprehensive diagnosis decision-making includes comprehensive solutions and optimization strategies for fault handling.
逻辑回归分析模块逻辑回归算法，结合贝叶斯优化技术，对HPLC设备的运行数据进行深入分析，它能够提供故障类型的精确预测，使得故障响应更为及时和针对性，在提高故障预测的准确性和可靠性方面起着至关重要的作用。The logistic regression algorithm of the logistic regression analysis module, combined with Bayesian optimization technology, conducts in-depth analysis of the operating data of HPLC equipment. It can provide accurate predictions of fault types, make fault responses more timely and targeted, and improve the accuracy of fault predictions. performance and reliability.
波动监控模块采用自回归移动平均模型和卡尔曼滤波器，该模块专注于实时监控设备性能参数的波动，不仅增强了对设备性能的控制，还能快速识别并报告任何异常状态，从而有效地预防和减少潜在故障。The fluctuation monitoring module uses an autoregressive moving average model and a Kalman filter. This module focuses on real-time monitoring of fluctuations in equipment performance parameters. It not only enhances the control of equipment performance, but also quickly identifies and reports any abnormal status, thereby effectively preventing and reduce potential failures.
多阶段管理模块利用模糊逻辑控制和隐马尔可夫模型，该模块能够根据设备的不同运行阶段，如启动、运行和关闭，自动调整检测参数，确保了在不同运行条件下的最佳故障检测效率和准确性。The multi-stage management module uses fuzzy logic control and hidden Markov models. This module can automatically adjust detection parameters according to the different operating stages of the equipment, such as startup, operation and shutdown, ensuring the best fault detection efficiency under different operating conditions. and accuracy.
递归诊断模块结合递归神经网络和决策树，该模块提供了一个深入和层次化的故障分析方法，它通过迭代地分析故障原因，能够精确地定位故障源，并提供详细的故障分析报告。The recursive diagnosis module combines recursive neural networks and decision trees. This module provides an in-depth and hierarchical fault analysis method. By iteratively analyzing the cause of the fault, it can accurately locate the fault source and provide a detailed fault analysis report.
流形学习模块应用局部线性嵌入和等度量映射技术，该模块专门用于深入分析和揭示故障数据的内在结构，这对于理解复杂的故障模式和数据的多维性至关重要。The manifold learning module applies local linear embedding and equimetric mapping techniques. This module is specially designed to deeply analyze and reveal the intrinsic structure of failure data, which is crucial for understanding complex failure modes and the multidimensionality of data.
故障模式识别模块通过结合支持向量机和K-最近邻算法，此模块不仅能细化和分类故障模式，而且还能为后续的故障处理提供详尽的指导，对于制定有效的维护策略极为关键。The fault mode identification module combines the support vector machine and the K-nearest neighbor algorithm. This module can not only refine and classify fault modes, but also provide detailed guidance for subsequent fault handling, which is extremely critical for formulating effective maintenance strategies.
综合决策模块将前述所有模块的输出结果汇集起来，采用知识图谱辅助的决策分析和决策树技术，为故障诊断和处理提供全面的决策支持，不仅提升了决策的全面性和深度，还确保了故障处理方案的优化和实用性。The comprehensive decision-making module brings together the output results of all the aforementioned modules and uses knowledge graph-assisted decision analysis and decision tree technology to provide comprehensive decision support for fault diagnosis and processing. It not only improves the comprehensiveness and depth of decision-making, but also ensures that faults are Optimization and practicality of treatment options.
请参阅图3，逻辑回归分析模块包括模型训练子模块、预测分析子模块、模型优化子模块；Please refer to Figure 3. The logistic regression analysis module includes a model training sub-module, a predictive analysis sub-module, and a model optimization sub-module;
模型训练子模块基于HPLC设备的运行数据，采用罗吉斯回归模型，并结合权重调整技术，对故障模式进行初步分析和模型建立，生成初步故障模式模型；The model training sub-module uses the Logis regression model and weight adjustment technology based on the operating data of the HPLC equipment to conduct preliminary analysis and model establishment of the failure mode and generate a preliminary failure mode model;
预测分析子模块基于初步故障模式模型，采用高斯分布分析，再结合条件概率方法，进行故障类型的概率预测分析，生成故障类型概率分析结果；The prediction analysis sub-module is based on the preliminary failure mode model, uses Gaussian distribution analysis, and combines with the conditional probability method to perform probabilistic prediction analysis of fault types and generate failure type probability analysis results;
模型优化子模块基于故障类型概率分析结果，采用贝叶斯网络和梯度提升算法，调整模型参数，并优化预测性能，生成故障类型预测结果。Based on the fault type probability analysis results, the model optimization sub-module uses Bayesian network and gradient boosting algorithm to adjust model parameters, optimize prediction performance, and generate fault type prediction results.
模型训练子模块基于HPLC设备的运行数据，采用罗吉斯回归模型进行初始分析，通过结合权重调整技术，这一阶段的目标是建立一个初步的故障模式模型，这涉及收集和分析大量运行数据，识别出与故障相关的关键变量，以及设定和调整模型的权重参数，以增强模型对故障模式的识别能力。The model training sub-module uses the Logis regression model for initial analysis based on the operating data of the HPLC equipment. By combining the weight adjustment technology, the goal of this stage is to establish a preliminary failure mode model, which involves collecting and analyzing a large amount of operating data. Identify key variables related to faults, and set and adjust the weight parameters of the model to enhance the model's ability to identify fault modes.
预测分析子模块以初步建立的故障模式模型为基础，采用高斯分布分析和条件概率方法，其核心是利用统计方法对每种可能的故障类型进行概率预测分析，这不仅涉及计算各种故障的概率，还包括评估这些概率的准确性和可靠性，从而生成故障类型的概率分布分析结果。The predictive analysis sub-module is based on the initially established failure mode model and uses Gaussian distribution analysis and conditional probability methods. Its core is to use statistical methods to conduct probabilistic prediction analysis for each possible failure type, which not only involves calculating the probability of various failures , also includes evaluating the accuracy and reliability of these probabilities to generate probability distribution analysis results of fault types.
模型优化子模块根据故障类型的概率分析结果，进一步精炼和优化预测模型，这里采用贝叶斯网络和梯度提升算法，调整模型参数，增强模型对新数据的适应能力和预测准确性，优化后的模型能够更准确地预测故障类型，为后续的故障处理提供更可靠的决策支持。The model optimization sub-module further refines and optimizes the prediction model based on the probability analysis results of fault types. Here, Bayesian network and gradient boosting algorithm are used to adjust model parameters and enhance the model's adaptability to new data and prediction accuracy. The optimized The model can predict fault types more accurately and provide more reliable decision support for subsequent fault handling.
请参阅图4，波动监控模块包括实时监控子模块、波动分析子模块、异常检测子模块；Please refer to Figure 4. The fluctuation monitoring module includes real-time monitoring sub-module, fluctuation analysis sub-module, and anomaly detection sub-module;
实时监控子模块基于故障类型预测结果，采用流数据处理技术，对设备性能参数进行实时监控，生成实时性能数据报告；Based on the fault type prediction results, the real-time monitoring sub-module uses streaming data processing technology to monitor equipment performance parameters in real time and generate real-time performance data reports;
波动分析子模块基于实时性能数据报告，采用自回归积分滑动平均模型，对设备参数波动进行分析，生成波动趋势分析报告；The fluctuation analysis sub-module is based on the real-time performance data report and uses the autoregressive integral moving average model to analyze the fluctuations of equipment parameters and generate a fluctuation trend analysis report;
异常检测子模块基于波动趋势分析报告，采用多元卡尔曼滤波技术，识别并定位异常波动，生成波动分析报告。The anomaly detection sub-module is based on the fluctuation trend analysis report and uses multivariate Kalman filtering technology to identify and locate abnormal fluctuations and generate a fluctuation analysis report.
实时监控子模块基于故障类型预测结果，运用流数据处理技术对设备性能参数进行实时监控，因此，该模块能够持续跟踪和记录设备的关键性能指标，如压力、温度和流速等，从而生成实时性能数据报告，确保了对任何微小的性能变化的即时捕捉，为后续的波动分析提供了必要的数据基础。The real-time monitoring sub-module uses stream data processing technology to monitor equipment performance parameters in real time based on fault type prediction results. Therefore, this module can continuously track and record key performance indicators of equipment, such as pressure, temperature, flow rate, etc., thereby generating real-time performance Data reporting ensures the immediate capture of any small performance changes, providing the necessary data basis for subsequent fluctuation analysis.
波动分析子模块以实时监控所得的性能数据为基础，采用自回归积分滑动平均模型对设备参数的波动进行深入分析，其重点在于识别和量化性能参数的波动趋势，评估这些波动是否超出了正常范围，最后，模块能够生成波动趋势分析报告，为异常状态的早期识别奠定基础。The fluctuation analysis sub-module is based on the performance data obtained from real-time monitoring and uses the autoregressive integral moving average model to conduct in-depth analysis of the fluctuations of equipment parameters. Its focus is to identify and quantify the fluctuation trends of performance parameters and evaluate whether these fluctuations exceed the normal range. ,Finally, the module can generate a fluctuation trend ,analysis report, laying the foundation for early identification of ,abnormal states.
异常检测子模块利用多元卡尔曼滤波技术，基于波动趋势分析报告中的数据，对异常波动进行识别和定位，从而使得模块不仅能够精确识别出异常波动，还能确定其具体位置和可能的原因，最后，该模块生成详细的波动分析报告，为采取适当的维护措施提供了关键信息。The anomaly detection sub-module uses multivariate Kalman filtering technology to identify and locate abnormal fluctuations based on the data in the fluctuation trend analysis report, so that the module can not only accurately identify abnormal fluctuations, but also determine its specific location and possible causes. Finally, the module generates detailed fluctuation analysis reports, providing critical information for taking appropriate maintenance actions.
请参阅图5，多阶段管理模块包括初始化检测子模块、稳态运行子模块、运行调整子模块；Please refer to Figure 5. The multi-stage management module includes an initialization detection sub-module, a steady-state operation sub-module, and an operation adjustment sub-module;
初始化检测子模块基于波动分析报告，采用主成分分析，评估设备启动时的性能状况，生成启动阶段诊断结果；The initialization detection sub-module is based on the fluctuation analysis report and uses principal component analysis to evaluate the performance of the equipment when it is started and generate diagnostic results during the startup phase;
稳态运行子模块基于启动阶段诊断结果，采用时间序列分析和趋势监测技术，评估设备运行期间的表现，生成稳态运行分析报告；The steady-state operation sub-module uses time series analysis and trend monitoring technology to evaluate the performance of the equipment during operation based on the diagnosis results during the startup phase, and generates a steady-state operation analysis report;
运行调整子模块基于稳态运行分析报告，采用模糊逻辑控制器和动态条件随机场算法，自动调整检测参数，并适应多种运行状态，生成阶段性故障诊断结果。Based on the steady-state operation analysis report, the operation adjustment sub-module uses fuzzy logic controller and dynamic condition random field algorithm to automatically adjust detection parameters, adapt to various operating states, and generate phased fault diagnosis results.
初始化检测子模块针对设备启动阶段进行性能评估，通过基于波动分析报告的主成分分析，该子模块能够准确评估设备启动时的性能状况，如启动过程中的压力和温度变化等关键指标，这一评估帮助识别任何可能在启动阶段出现的异常，从而生成详细的启动阶段诊断结果，为后续阶段的监测和调整提供基础数据。The initialization detection sub-module evaluates the performance of the equipment during the startup phase. Through principal component analysis based on the fluctuation analysis report, this sub-module can accurately evaluate the performance of the equipment when it starts, such as key indicators such as pressure and temperature changes during the startup process. This The assessment helps identify any anomalies that may occur during the startup phase, thereby generating detailed diagnostic results during the startup phase and providing basic data for monitoring and adjustment in subsequent phases.
稳态运行子模块基于启动阶段的诊断结果，运用时间序列分析和趋势监测技术，对设备在正常运行期间的性能表现进行评估，该子模块专注于分析设备在稳态条件下的运行数据，如流速的一致性、检测器的响应等，以确保设备在整个运行周期内的性能稳定性和效率，通过这样的分析，生成的稳态运行分析报告为设备的持续监控和即时调整提供了详尽的信息。The steady-state operation sub-module uses time series analysis and trend monitoring technology to evaluate the performance of the equipment during normal operation based on the diagnostic results during the startup phase. This sub-module focuses on analyzing the operation data of the equipment under steady-state conditions, such as The consistency of the flow rate, the response of the detector, etc., to ensure the performance stability and efficiency of the equipment throughout the entire operating cycle. Through such analysis, the generated steady-state operation analysis report provides detailed information for continuous monitoring and instant adjustment of the equipment. information.
运行调整子模块基于稳态运行分析报告，利用模糊逻辑控制器和动态条件随机场算法，自动调整检测参数，适应设备的多种运行状态，该子模块的功能是在设备运行过程中动态调整检测策略，以应对各种可能的变化和潜在的故障，使得系统能够灵活地应对不同的运行情况，及时发现并诊断阶段性的故障，从而生成阶段性故障诊断结果。Based on the steady-state operation analysis report, the operation adjustment sub-module uses fuzzy logic controller and dynamic condition random field algorithm to automatically adjust detection parameters to adapt to various operating states of the equipment. The function of this sub-module is to dynamically adjust detection during the operation of the equipment. Strategies to cope with various possible changes and potential failures enable the system to flexibly respond to different operating conditions, discover and diagnose phased faults in a timely manner, and generate phased fault diagnosis results.
请参阅图6，递归诊断模块包括初步诊断子模块、迭代分析子模块、决策树导向子模块；Please refer to Figure 6. The recursive diagnosis module includes a preliminary diagnosis sub-module, an iterative analysis sub-module, and a decision tree guidance sub-module;
初步诊断子模块基于阶段性故障诊断结果，采用深度递归神经网络，对故障进行初始分析，并进行数据融合，生成初步故障分析报告；Based on the staged fault diagnosis results, the preliminary diagnosis sub-module uses a deep recursive neural network to conduct initial analysis of the fault, conducts data fusion, and generates a preliminary fault analysis report;
迭代分析子模块基于初步故障分析报告，采用长短期记忆网络和多层感知机进行迭代分析，并探索故障的原因，生成故障原因分析报告；The iterative analysis sub-module uses the long short-term memory network and multi-layer perceptron to conduct iterative analysis based on the preliminary fault analysis report, explores the cause of the fault, and generates a fault cause analysis report;
决策树导向子模块基于故障原因分析报告，采用随机森林对故障原因进行结构化排序和分类，生成迭代诊断报告。Based on the fault cause analysis report, the decision tree guidance sub-module uses random forest to structurally sort and classify the fault causes and generate an iterative diagnosis report.
初步诊断子模块基于多阶段管理模块提供的阶段性故障诊断结果，运用深度递归神经网络对故障进行初步分析，这个过程包括对各种故障迹象和数据的综合处理和融合，目的是识别出故障的初步特征和可能的成因，生成初步故障分析报告，这一步是诊断流程的关键起点，为更深入的分析奠定了基础。Based on the staged fault diagnosis results provided by the multi-stage management module, the preliminary diagnosis sub-module uses a deep recursive neural network to conduct a preliminary analysis of the fault. This process includes the comprehensive processing and fusion of various fault signs and data, with the purpose of identifying the cause of the fault. Preliminary characteristics and possible causes are generated to generate a preliminary fault analysis report. This step is the key starting point of the diagnostic process and lays the foundation for more in-depth analysis.
迭代分析子模块接手初步故障分析报告，采用长短期记忆网络和多层感知机进行更深层次的迭代分析，在这一阶段，系统不断迭代和深化对故障原因的探索，逐层剥离可能的故障因素，从而生成更为详尽和深入的故障原因分析报告，并且使得故障诊断不仅仅停留在表面，而是能够触及更深层。The iterative analysis sub-module takes over the preliminary fault analysis report and uses long short-term memory network and multi-layer perceptron to conduct deeper iterative analysis. At this stage, the system continuously iterates and deepens the exploration of the cause of the fault, peeling off possible fault factors layer by layer. , thereby generating a more detailed and in-depth fault cause analysis report, and enabling fault diagnosis to not only stay on the surface, but to reach deeper levels.
决策树导向子模块以故障原因分析报告为基础，运用随机森林算法进行结构化的故障原因排序和分类，这个过程中，系统通过决策树的方法，将复杂的故障原因进行结构化处理，清晰地展现出各种故障原因的相互关系和优先级，最终生成迭代诊断报告，从而使得故障诊断结果不仅准确，而且条理清晰，便于后续的处理和维护决策。The decision tree guidance sub-module is based on the fault cause analysis report and uses the random forest algorithm to conduct structured sorting and classification of fault causes. In this process, the system uses the decision tree method to structurally process complex fault causes and clearly It shows the interrelationship and priority of various fault causes, and finally generates an iterative diagnosis report, so that the fault diagnosis results are not only accurate, but also clear and convenient for subsequent processing and maintenance decisions.
请参阅图7，流形学习模块包括数据降维子模块、模式分析子模块、故障分类子模块；Please refer to Figure 7. The manifold learning module includes a data dimensionality reduction sub-module, a pattern analysis sub-module, and a fault classification sub-module;
数据降维子模块基于迭代诊断报告，采用局部线性嵌入技术，对故障数据进行降维处理，并突显关键特征，生成降维后的故障数据；The data dimensionality reduction sub-module is based on the iterative diagnosis report and uses local linear embedding technology to reduce the dimensionality of the fault data, highlight key features, and generate dimensionally reduced fault data;
模式分析子模块基于降维后的故障数据，采用等度量映射技术，探究故障模式的内在联系，生成故障模式分析报告；Based on the dimensionally reduced fault data, the pattern analysis sub-module uses equal metric mapping technology to explore the internal connections of the fault modes and generate a fault mode analysis report;
故障分类子模块基于故障模式分析报告，采用谱聚类算法，对故障模式进行分类，生成故障模式识别结果。The fault classification sub-module uses the spectral clustering algorithm to classify fault modes based on the fault mode analysis report and generates fault mode identification results.
数据降维子模块接收来自迭代诊断模块的报告，并运用局部线性嵌入技术对故障数据进行降维处理，减少了数据的复杂性，同时保留关键特征，以便更清晰地突显故障模式的本质，因此，子模块能够有效地提取和突出那些对于故障诊断至关重要的数据特征，生成了更加简洁且信息丰富的降维后故障数据。The data dimensionality reduction sub-module receives the report from the iterative diagnosis module and uses local linear embedding technology to reduce the dimensionality of the fault data, reducing the complexity of the data while retaining key features to more clearly highlight the essence of the fault mode. Therefore , the sub-module can effectively extract and highlight those data features that are crucial for fault diagnosis, and generate more concise and information-rich fault data after dimensionality reduction.
模式分析子模块基于这些降维后的故障数据，采用等度量映射技术进一步探究故障模式的内在联系，同时深入挖掘故障数据中隐含的结构和模式，揭示故障的内在规律，这种深度的模式分析对于理解故障的根本原因和复杂性至关重要，子模块最终生成了详尽的故障模式分析报告。Based on these dimensionally reduced fault data, the pattern analysis sub-module uses equimetric mapping technology to further explore the intrinsic relationships of fault modes. At the same time, it deeply explores the structures and patterns hidden in the fault data to reveal the intrinsic laws of faults. This deep pattern Analysis is critical to understanding the root cause and complexity of the failure, and the sub-module culminates in a detailed failure mode analysis report.
故障分类子模块以故障模式分析报告为基础，运用谱聚类算法对不同的故障模式进行分类，并且涉及将故障模式按照它们的相似性和差异性进行分组，以便于后续的处理和维护决策，因此，子模块能够清晰地识别和区分不同的故障类型，生成精确的故障模式识别结果。The fault classification sub-module is based on the fault mode analysis report, uses spectral clustering algorithm to classify different fault modes, and involves grouping fault modes according to their similarities and differences to facilitate subsequent processing and maintenance decisions. Therefore, the sub-module is able to clearly identify and distinguish different fault types and generate accurate fault mode identification results.
请参阅图8，故障模式识别模块包括模式对比子模块、模式验证子模块、结果细化子模块；Please refer to Figure 8. The fault mode identification module includes a mode comparison sub-module, a mode verification sub-module, and a result refinement sub-module;
模式对比子模块基于故障模式识别结果，采用模式匹配技术，对比分析多类故障模式之间的相似性和差异性，生成模式对比分析结果；Based on the fault mode recognition results, the mode comparison sub-module uses pattern matching technology to comparatively analyze the similarities and differences between multiple types of fault modes, and generates mode comparison analysis results;
模式验证子模块基于模式对比分析结果，采用交叉验证和统计验证技术，对故障模式的可靠性进行确认，生成模式验证报告；Based on the pattern comparison analysis results, the pattern verification sub-module uses cross-validation and statistical verification techniques to confirm the reliability of the failure mode and generate a pattern verification report;
结果细化子模块基于模式验证报告，采用支持向量机和K-最近邻算法，对故障模式进行分析和分类调整，生成细化的故障模式分析报告。The result refinement sub-module uses the support vector machine and K-nearest neighbor algorithm to analyze and classify the fault modes based on the mode verification report, and generates a refined fault mode analysis report.
模式对比子模块基于流形学习模块提供的故障模式识别结果，运用模式匹配技术进行深入分析，该子模块通过对比不同故障模式之间的相似性和差异性，有效地识别出各种故障模式的特点和关系，这种对比分析帮助明确不同故障模式之间的界限，提升了故障模式理解的深度和广度，从而生成了详尽的模式对比分析结果。The mode comparison sub-module uses pattern matching technology to conduct in-depth analysis based on the fault mode recognition results provided by the manifold learning module. This sub-module effectively identifies the characteristics of various fault modes by comparing the similarities and differences between different fault modes. Characteristics and relationships, this comparative analysis helps clarify the boundaries between different failure modes, improves the depth and breadth of failure mode understanding, and generates detailed mode comparative analysis results.
模式验证子模块接收模式对比分析的结果，并采用交叉验证和统计验证技术进一步确认故障模式的可靠性，其核心在于确保识别出的故障模式具有高度的准确性和可靠性，通过这种严格的验证流程，子模块能够生成经过验证的模式验证报告，为后续的故障处理提供了坚实的基础。The mode verification sub-module receives the results of mode comparison analysis and uses cross-validation and statistical verification techniques to further confirm the reliability of the fault mode. Its core is to ensure that the identified fault mode has a high degree of accuracy and reliability. Through this strict Verification process, sub-module can generate a verified pattern verification report, providing a solid foundation for subsequent troubleshooting.
结果细化子模块基于模式验证报告，采用支持向量机和K-最近邻算法对故障模式进行进一步的分析和细化分类，这个过程不仅涉及对故障模式的深入理解，还包括对故障模式的调整和优化，以确保最终的分类结果既精确又实用，通过这种高级的分析和分类技术，子模块能够生成更细化和精确的故障模式结果。The result refinement sub-module uses the support vector machine and K-nearest neighbor algorithm to further analyze and refine the classification of the fault mode based on the mode verification report. This process not only involves an in-depth understanding of the fault mode, but also includes the adjustment of the fault mode. and optimization to ensure that the final classification results are both accurate and practical. Through this advanced analysis and classification technology, the sub-modules are able to generate more refined and precise failure mode results.
请参阅图9，综合决策模块包括结果汇总子模块、决策制定子模块、优化策略子模块；Please refer to Figure 9. The comprehensive decision-making module includes a result summary sub-module, a decision-making sub-module, and an optimization strategy sub-module;
结果汇总子模块基于细化的故障模式分析报告，综合逻辑回归分析模块、波动监控模块、多阶段管理模块、递归诊断模块、流形学习模块的输出结果，采用数据聚合技术和主成分分析，进行数据归纳汇总，生成综合数据报告；The result summary sub-module is based on the detailed failure mode analysis report, comprehensively integrates the output results of the logistic regression analysis module, fluctuation monitoring module, multi-stage management module, recursive diagnosis module, and manifold learning module, and uses data aggregation technology and principal component analysis to conduct Summarize data and generate comprehensive data reports;
决策制定子模块基于综合数据报告，采用知识图谱辅助的决策分析和Gini指数分析，对综合数据进行解析，并提出故障诊断和处理方案，生成初步决策方案；Based on the comprehensive data report, the decision-making sub-module uses knowledge graph-assisted decision analysis and Gini index analysis to analyze the comprehensive data, propose fault diagnosis and treatment plans, and generate preliminary decision-making plans;
优化策略子模块基于初步决策方案，采用模拟退火优化算法和场景分析技术，细化和优化决策方案内容，生成综合诊断决策。Based on the preliminary decision-making plan, the optimization strategy sub-module uses simulated annealing optimization algorithm and scene analysis technology to refine and optimize the content of the decision-making plan and generate comprehensive diagnostic decisions.
结果汇总子模块基于细化的故障模式分析报告，综合了逻辑回归分析模块、波动监控模块、多阶段管理模块、递归诊断模块和流形学习模块的输出结果，通过采用数据聚合技术和主成分分析，该子模块对这些来自不同模块的数据进行归纳和汇总，从而生成了包含了全面信息的综合数据报告，为决策提供了一个全面的数据基础。The result summary sub-module is based on a detailed failure mode analysis report, integrating the output results of the logistic regression analysis module, fluctuation monitoring module, multi-stage management module, recursive diagnosis module and manifold learning module. By using data aggregation technology and principal component analysis , this sub-module summarizes and summarizes the data from different modules, thereby generating a comprehensive data report containing comprehensive information, providing a comprehensive data basis for decision-making.
决策制定子模块以综合数据报告为基础，运用知识图谱辅助的决策分析和Gini指数分析进行深入解析，利用高级分析技术，对综合数据进行解析，从而提出针对性的故障诊断和处理方案，通过这种方法，生成了具有明确指导意义的初步决策方案，为故障的有效处理提供了方向。The decision-making sub-module is based on the comprehensive data report, uses knowledge graph-assisted decision analysis and Gini index analysis for in-depth analysis, and uses advanced analysis technology to analyze the comprehensive data, thereby proposing targeted fault diagnosis and processing plans. Through this This method generates a preliminary decision-making plan with clear guiding significance and provides a direction for effective fault handling.
优化策略子模块接手初步决策方案，采用模拟退火优化算法和场景分析技术对决策方案进行进一步细化和优化，这个过程涉及评估不同故障处理方案的效果和风险，以及调整和优化决策，以确保最终的故障处理方案是最有效和最适合的，从而使得最终生成的综合诊断决策既实用又高效。The optimization strategy sub-module takes over the preliminary decision-making plan and uses simulated annealing optimization algorithm and scenario analysis technology to further refine and optimize the decision-making plan. This process involves evaluating the effects and risks of different fault handling plans, as well as adjusting and optimizing decisions to ensure the final The troubleshooting solution is the most effective and appropriate, making the final comprehensive diagnostic decision both practical and efficient.
请参阅图10，一种HPLC故障诊断设备的检测方法，HPLC故障诊断设备的检测方法基于上述HPLC故障诊断设备的检测系统执行，包括以下步骤：Please refer to Figure 10, a detection method of HPLC fault diagnosis equipment. The detection method of HPLC fault diagnosis equipment is based on the above detection system of HPLC fault diagnosis equipment and includes the following steps:
S1：基于HPLC设备的运行数据，采用逻辑回归算法，结合贝叶斯优化技术对故障模式进行分析，并进行模式的识别，生成故障类型预测结果；S1: Based on the operating data of HPLC equipment, use logistic regression algorithm and Bayesian optimization technology to analyze the fault mode, identify the mode, and generate fault type prediction results;
S2：基于故障类型预测结果，采用自回归移动平均模型和卡尔曼滤波器，进行设备性能参数的波动分析，识别并记录异常情况，生成波动分析报告；S2: Based on the fault type prediction results, use the autoregressive moving average model and Kalman filter to conduct fluctuation analysis of equipment performance parameters, identify and record abnormal conditions, and generate a fluctuation analysis report;
S3：基于波动分析报告，采用模糊逻辑控制和隐马尔可夫模型，根据设备运行阶段自动调整检测参数，生成阶段性故障诊断结果；S3: Based on the fluctuation analysis report, fuzzy logic control and hidden Markov model are used to automatically adjust detection parameters according to the equipment operating stage and generate phased fault diagnosis results;
S4：基于阶段性故障诊断结果，采用递归神经网络，结合决策树分析对故障进行迭代诊断，并探究故障原因，生成迭代诊断报告；S4: Based on the staged fault diagnosis results, use recursive neural network and decision tree analysis to iteratively diagnose the fault, explore the cause of the fault, and generate an iterative diagnosis report;
S5：基于迭代诊断报告，采用局部线性嵌入和等度量映射技术，分析故障模式，并揭示数据的内在结构，生成故障模式识别结果；S5: Based on the iterative diagnosis report, use local linear embedding and equimetric mapping technology to analyze the fault mode, reveal the intrinsic structure of the data, and generate fault mode identification results;
S6：基于故障模式识别结果，采用支持向量机和K-最近邻算法，对故障模式进行细化分析和分类，并为故障处理提供指导，生成细化的故障模式分析报告；S6: Based on the fault mode recognition results, use support vector machine and K-nearest neighbor algorithm to conduct detailed analysis and classification of fault modes, provide guidance for fault handling, and generate a detailed fault mode analysis report;
S7：基于细化的故障模式分析报告，综合故障模式识别结果、迭代诊断报告、阶段性故障诊断结果、波动分析报告、故障类型预测结果的内容，采用知识图谱辅助的决策分析和Gini指数分析，提出故障诊断和处理方案，生成初步决策方案；S7: Based on the detailed failure mode analysis report, comprehensive failure mode identification results, iterative diagnosis reports, staged fault diagnosis results, fluctuation analysis reports, and fault type prediction results, using knowledge graph-assisted decision analysis and Gini index analysis, Propose fault diagnosis and treatment plans, and generate preliminary decision-making plans;
S8：基于初步决策方案，采用模拟退火优化算法和场景分析技术，对决策方案进行细化和优化，生成综合诊断决策。S8: Based on the preliminary decision-making plan, use simulated annealing optimization algorithm and scene analysis technology to refine and optimize the decision-making plan and generate a comprehensive diagnostic decision.
通过步骤S1逻辑回归算法结合贝叶斯优化进行故障模式的分析和识别，能够精准预测各种故障类型，使得故障响应更加及时和针对性。不仅减少了设备的停机时间，也显著提高了故障处理的效率。Through step S1 logistic regression algorithm combined with Bayesian optimization to analyze and identify fault modes, various fault types can be accurately predicted, making fault response more timely and targeted. Not only does it reduce equipment downtime, it also significantly improves the efficiency of troubleshooting.
步骤S2和S3的实施，通过自回归移动平均模型和卡尔曼滤波器进行波动分析，以及模糊逻辑控制和隐马尔可夫模型自动调整检测参数，增强了对设备性能波动的监控能力，并能根据设备的运行阶段做出快速调整。这样的适应性和灵活性对于维持设备的稳定运行和及时识别潜在问题至关重要。The implementation of steps S2 and S3, through the autoregressive moving average model and Kalman filter for fluctuation analysis, as well as the fuzzy logic control and hidden Markov model to automatically adjust the detection parameters, enhances the ability to monitor equipment performance fluctuations and can based on Make quick adjustments during the operating phase of the equipment. Such adaptability and flexibility are critical to maintaining stable operation of equipment and identifying potential problems in a timely manner.
步骤S4和S5的递归神经网络和局部线性嵌入技术，结合决策树分析和等度量映射，提供了深入挖掘故障原因和分析故障模式的能力。这种深度分析方法不仅提升了诊断的精确度，而且有助于揭示故障的内在结构，为后续的处理提供了重要的洞见。The recurrent neural network and local linear embedding technology of steps S4 and S5, combined with decision tree analysis and equimetric mapping, provide the ability to deeply dig into the cause of faults and analyze fault modes. This in-depth analysis method not only improves the accuracy of diagnosis, but also helps reveal the internal structure of the fault, providing important insights for subsequent processing.
步骤S6的支持向量机和K-最近邻算法的应用，进一步细化和分类故障模式，为故障处理提供了更为精确的指导。这一步骤增强了故障分类的细致度，使得每一种故障都能得到适当的响应和处理。The application of support vector machine and K-nearest neighbor algorithm in step S6 further refines and classifies the fault modes, providing more precise guidance for fault handling. This step enhances the granularity of fault classification so that each fault can be appropriately responded to and handled.
步骤S7和S8的综合决策过程，采用知识图谱辅助的决策分析、Gini指数分析以及模拟退火优化算法和场景分析技术，不仅确保了决策方案的全面性和深入性，还对决策方案进行了细化和优化。这样的综合性决策过程显著提高了故障处理方案的效果和适用性。The comprehensive decision-making process of steps S7 and S8 uses knowledge graph-assisted decision analysis, Gini index analysis, simulated annealing optimization algorithm and scenario analysis technology, which not only ensures the comprehensiveness and in-depthness of the decision-making plan, but also refines the decision-making plan. and optimization. Such a comprehensive decision-making process significantly improves the effectiveness and applicability of troubleshooting solutions.
以上，仅是本发明的较佳实施例而已，并非对本发明作其他形式的限制，任何熟悉本专业的技术人员可能利用上述揭示的技术内容加以变更或改型为等同变化的等效实施例应用于其他领域，但是凡是未脱离本发明技术方案内容，依据本发明的技术实质对以上实施例所做的任何简单修改、等同变化与改型，仍属于本发明技术方案的保护范围。The above are only preferred embodiments of the present invention and do not limit the present invention in other forms. Any skilled person familiar with the art may use the technical content disclosed above to make changes or modifications to equivalent embodiments with equivalent changes. In other fields, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the technical content of the present invention still fall within the protection scope of the technical solution of the present invention.
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