CN111159487A - An Intelligent System for Predictive Maintenance of Automobile Engine Spindle - Google Patents

Abstract

Translated fromChinese

本发明公开了一种汽车发动机主轴预测性维护智能系统，包括传感器模块、信息采集模块、信息对比模块和信息决策模块，传感器模块用于对生产用的主轴进行监测，采集主轴数据方便智能维护；信息采集模块用于收集来自主轴上的传感器模块的信息数据；信息对比模块用于接收信息采集模块采集的信息，并且将信息进行对比处理，形成诊断结果，信息决策模块用于接收信息对比模块的诊断结果。本发明能够系统可以对车间设备的当前状态和使用寿命进行智能的诊断和管理，以图表可视化的形式公开在看板上，帮助指导维修部门对相应设备的日常维护和排产部门对生产计划的调整，降低了企业因设备故障造成的停产风险并提高维修人员的工作效率。The invention discloses an intelligent system for predictive maintenance of a main shaft of an automobile engine, which comprises a sensor module, an information acquisition module, an information comparison module and an information decision module. The sensor module is used for monitoring production main shafts, and collecting main shaft data is convenient for intelligent maintenance; The information collection module is used to collect the information data from the sensor module on the main shaft; the information comparison module is used to receive the information collected by the information collection module, and the information is compared and processed to form a diagnosis result, and the information decision module is used to receive the information of the information comparison module. diagnostic result. The invention can systematically perform intelligent diagnosis and management on the current state and service life of workshop equipment, and disclose it on the Kanban board in the form of chart visualization, helping to guide the maintenance department in the daily maintenance of the corresponding equipment and the production scheduling department in the adjustment of the production plan , reducing the risk of production downtime caused by equipment failures and improving the work efficiency of maintenance personnel.

Description

Predictive maintenance intelligent system for automobile engine spindle

Technical Field

The invention relates to the technical field of automobile engine spindles, in particular to an intelligent predictive maintenance system for an automobile engine spindle.

Background

The automobile engine is a machine for providing power for an automobile, is the heart of the automobile and influences the dynamic property, the economy and the environmental protection of the automobile, an engine main shaft refers to a shaft which receives power from an engine or a motor and transmits the power to other parts, the automobile engine main shaft is used as an important transmission component of the automobile, real-time maintenance and detection are needed to be carried out to ensure the normal production work of the automobile engine, the intelligent modification and predictive maintenance are carried out on the management of an equipment main shaft for producing the engine in the current automobile production and manufacturing process, namely, the collected data are processed, analyzed and summarized, and the summarized rule or experience is brought into a preventive maintenance plan for implementation. Traditional plant predictive maintenance relies heavily on the experience of service specialists. Therefore, a maintenance service department of a client enterprise hopes to accurately predict possible failure modes and service lives of equipment in a workshop by means of a big data technology and an artificial intelligence technology, help to realize standby state diagnosis and early warning, and even manage the life of the equipment in a whole period, so that the department is guided to make a decision to reduce the downtime of the equipment, and the emergency situation of stopping production in the workshop is avoided as much as possible.

Therefore, an intelligent system for predictive maintenance of the main shaft of the automobile engine is needed, the system can intelligently diagnose and manage the current state and the service life of workshop equipment, the system is disclosed on a billboard in a visual chart form and helps to guide maintenance departments to adjust the daily maintenance and production scheduling departments of corresponding equipment, the service life of the equipment is improved by twenty percent after the system is on line, the cost of each main shaft of the engine can be saved by about ten million yuan per year when the cost of each main shaft of the engine is hundreds of million yuan, more importantly, the production stopping risk of an enterprise caused by equipment faults is reduced, and the working efficiency of maintenance personnel is improved.

Disclosure of Invention

The invention aims to provide an intelligent predictive maintenance system for a main shaft of an automobile engine, which aims to solve the problems that the intelligent diagnosis and management of the current state and the service life of workshop equipment are improved, the intelligent predictive maintenance system is disclosed on a billboard in a visual chart form, and the work efficiency of maintenance personnel is improved by guiding the maintenance department to perform daily maintenance on corresponding equipment and adjusting a production plan by a scheduling department.

The invention is realized by the following steps: a predictive maintenance intelligent system for a main shaft of an automobile engine comprises a sensor module, an information acquisition module, an information comparison module and an information decision module, wherein the sensor module is used for monitoring the main shaft for production, and the main shaft data is acquired so as to facilitate intelligent maintenance;

the information acquisition module is used for collecting information data from the sensor module on the main shaft;

the information comparison module is used for receiving the information acquired by the information acquisition module and comparing the information to form a diagnosis result.

And the information decision module is used for receiving the diagnosis result of the information comparison module and displaying the optimized maintenance scheme through the comparison data.

Further, the sensor module includes the circularity sensor module, the speed sensor module, the color sensor module, the shape sensor module, the sound sensor module, position sensor module and temperature sensor module, through setting up the circularity sensor module on the main shaft, the speed sensor module, sound sensor module and temperature sensor module are arranged in gathering the circularity data in the main shaft processing, rotational speed data, sound data and temperature data, and through the color sensor module, shape sensor module and position sensor module conveniently confirm to be located the factory site position, detect position color and shape discernment, be convenient for confirm the position that needs to maintain.

And then through, and then through adopting the sensor of multiple different grade type for the application data of the collection main shaft equipment of full aspect, conveniently make the predictive contrast of maintenance through calculation and contrast, and then be favorable to improving the accuracy of prediction.

Furthermore, the information acquisition module comprises a voice recognition module, a quantity recognition module, a scene recognition module, an object recognition module, a color recognition module and a data information recognition module, so that the data characteristics on the main shaft sensor module can be recognized and received conveniently, the comparison and calculation of data are facilitated, and the maintenance efficiency is improved.

And then through, and then through scene recognition module, object recognition module, colour recognition module, be convenient for collect the position and the service information of equipment, and then with the stored data contrast in the database, confirm as concrete main shaft equipment, then through sound recognition module, quantity recognition module and data information recognition module, be convenient for collect the in service behavior of main shaft equipment, be convenient for make predictive maintenance.

Further, the information comparison module comprises the following comparison steps:

s1, data import, namely importing the main shaft information data collected by the information collection module into the information comparison module;

s2, receiving data, and receiving the main shaft information imported by the data;

s3, calculating and comparing the received main shaft information imported to the data in the designed data model to obtain a comparison value;

s4, data comparison, namely comparing the numerical value calculated by the data model with the stored numerical value preset with the data;

and S5, exporting data, displaying a data comparison result export information comparison module, displaying that maintenance is not needed if the numerical value is the same as the preset value, and displaying that maintenance is needed if the numerical value is different from the preset value.

And then, through data import, data reception, data model, data comparison and data export, the data information of the spindle equipment acquired by the information acquisition module is compared through data import, data reception, data model, data comparison and data export processes, so as to make diagnosis for the spindle equipment.

Further, the data model construction step is as follows:

s1, data acquisition, namely, data such as replacement and shutdown faults of spare parts are acquired by collecting daily maintenance data of the main shaft;

s2, analyzing the data, and classifying, analyzing and summarizing the collected data;

s3, modeling data, and summarizing different rules or experiences for different data types by analyzing different types of data so as to construct calculation models of different data types;

s4, storing data, wherein models constructed into different data types are stored to facilitate calling;

and S5, calling data, and calling calculation models of different data types according to different data types to conveniently perform information decision comparison.

Furthermore, the data acquisition module collects data of daily maintenance of the main shaft, spare part replacement, shutdown faults and the like, then, through data analysis, classification processing, analysis and summarization are carried out on the collected data, data modeling is carried out, different rules or experiences are summarized by analyzing different types of data, so that calculation models of different data types are constructed, meanwhile, data storage is carried out, the models constructed into different data types are stored in a database, calling is convenient, data calling is carried out, and information decision comparison is convenient to carry out by calling the calculation models of different data types in the database according to different data types.

Furthermore, the information decision module comprises user login, data query, data judgment, database storage and data display, the information decision module can perform data query on data derived by the information comparison module after a user logs in, the past equipment daily maintenance data stored in the database, spare part replacement shutdown faults and the like are called when the information data is queried, the summarized solved rules or experiences are summarized, then the data judgment is performed according to the query result, the processed data and the scheme are stored in the database storage, the comparison and calling are convenient for the next time, the optimal solution is displayed through the data display, and detection personnel can conveniently detect according to the detection scheme.

Furthermore, the system organization is displayed through a tree structure to facilitate observation and adjustment by setting user management, mechanism management, area management, menu management, role management, dictionary management and operation logs in user login, the system organization is displayed through the tree structure to facilitate observation and adjustment, meanwhile, the system menu operation authority is configured, role menu authority distribution such as button authority identification and the like, and the role is set to divide data range authority according to the mechanism, so that visual information display and function limitation can be conveniently carried out on operators, data query can be carried out on data derived by an information comparison module after a user logs in, the conventional equipment daily maintenance data stored in a database, spare part replacement shutdown faults and the like and summarized solved rules or experiences can be called when the information data query is carried out, then data judgment is carried out according to query results, and then the processed data and the processed data are stored in the database, the next comparison and calling are facilitated, and then the optimal solution is displayed through data display, so that detection personnel can conveniently detect by referring to the detection scheme.

Further, the user login comprises user management, organization management, area management, menu management, role management, dictionary management and operation logs, and is used for completing user configuration, expanding company configuration and system city area model role menu authority distribution and operation record query in a tree structure.

Furthermore, the system organization is displayed through the tree structure, observation and adjustment are convenient to carry out, meanwhile, role menu authority distribution such as system menu operation authority, button authority identification and the like is configured, and the role is set to carry out data range authority division according to the organization, so that visual information display and function limitation are convenient for operators.

Further, the intelligent predictive maintenance method for the main shaft of the automobile engine comprises the following steps:

s1, the sensor module is used for monitoring the main shaft for production and collecting the main shaft data to facilitate intelligent maintenance;

s2, the information acquisition module is used for collecting information data from the sensor module on the main shaft;

and S3, the information comparison module is used for receiving the information collected by the information collection module and comparing the information to form a diagnosis result.

And S4, the information decision module is used for receiving the diagnosis result of the information comparison module and displaying the optimized maintenance scheme through the comparison data.

Furthermore, the current state and the service life of the workshop equipment can be intelligently diagnosed and managed, and the current state and the service life of the workshop equipment are disclosed on a billboard in a chart visualization mode, so that the method helps to guide a maintenance department to adjust the daily maintenance of the corresponding equipment and a production scheduling department to the production plan, and more importantly, the method reduces the production stop risk of an enterprise caused by equipment faults and improves the working efficiency of maintenance personnel.

Further, the information comparison module comprises a data model construction step:

s1, data acquisition, namely, data such as replacement and shutdown faults of spare parts are acquired by collecting daily maintenance data of the main shaft;

s2, analyzing the data, and classifying, analyzing and summarizing the collected data;

s3, modeling data, and summarizing different rules or experiences for different data types by analyzing different types of data so as to construct calculation models of different data types;

s4, storing data, wherein models constructed into different data types are stored to facilitate calling;

and S5, calling data, and calling calculation models of different data types according to different data types to conveniently perform information decision comparison.

And then through the data acquisition module, through collecting main shaft daily maintenance data, spare part change data such as shut down the trouble, then through data analysis, through categorised processing, analysis summarization to the data that collect, and data modeling, summarize different rules or experience with different data types through analyzing different data of type, thereby constitute different data type's calculation model, data storage simultaneously, through constructing different data type's model and storing in the database, conveniently call, data call, conveniently carry out information decision-making and contrast through the calculation model of different data types in the database of calling according to different data types.

Furthermore, the information decision module comprises user login, data query, data judgment, database storage and data display, the information decision module can perform data query on data derived by the information comparison module after a user logs in, the past equipment daily maintenance data stored in the database, spare part replacement shutdown faults and the like are called when the information data is queried, the summarized solved rules or experiences are summarized, then the data judgment is performed according to the query result, the processed data and the scheme are stored in the database storage, the comparison and calling are convenient for the next time, the optimal solution is displayed through the data display, and detection personnel can conveniently detect according to the detection scheme.

Compared with the prior art, the invention has the beneficial effects that: the invention can intelligently diagnose and manage the current state and the service life of workshop equipment, and is disclosed on a billboard in a chart visualization mode to help guide maintenance departments to adjust the daily maintenance of corresponding equipment and production scheduling departments to the production plan, more importantly, the invention reduces the production stop risk of enterprises caused by equipment faults and improves the working efficiency of maintenance personnel.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

FIG. 1 is a schematic view of the flow structure of the present invention;

FIG. 2 is a schematic view of the flow structure of an information collection module according to the present invention;

FIG. 3 is a schematic diagram of a sensor module flow structure according to the present invention;

FIG. 4 is a schematic diagram of the flow structure of the information comparison module according to the present invention;

FIG. 5 is a flow chart of an information decision module according to the present invention;

FIG. 6 is a schematic diagram of the construction method of the data model of the present invention;

in the figure: 1. a sensor module; 11. a roundness sensor module; 12. a rotational speed sensor; 13. a color sensor; 14. a shape sensor; 15. a sound sensor; 16. a position sensor; 17. a temperature sensor; 2. an information acquisition module; 21. a voice recognition module; 22. a quantity identification module; 23. a scene recognition module; 24. an object recognition module; 25. a color recognition module; 26. a data information identification module; 3. an information comparison module; 31. importing data; 32. receiving data; 33. a data model; 331. collecting data; 332. analyzing data; 333. modeling data; 334. storing data; 335. calling data; 34. data comparison; 35. exporting data; 4. an information decision model; 41. a user logs in; 42. querying data; 43. judging data; 44. storing a database; 45. and (6) displaying the data.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.

Referring to fig. 1, fig. 2, fig. 3, fig. 4, fig. 5 and fig. 6, an intelligent system for predictive maintenance of a main shaft of an automobile engine includes a sensor module 1, aninformation acquisition module 2, aninformation comparison module 3 and aninformation decision module 4, wherein the sensor module 1 is used for monitoring a main shaft for production and acquiring data of the main shaft to facilitate intelligent maintenance;

theinformation acquisition module 2 is used for collecting information data from the sensor module 1 on the main shaft;

theinformation comparison module 3 is used for receiving the information acquired by theinformation acquisition module 2 and comparing the information to form a diagnosis result.

Theinformation decision module 4 is used for receiving the diagnosis result of theinformation comparison module 3 and then displaying the optimized maintenance scheme through comparison data.

With the rapid development of the internet industry, in order to ensure the normal production work of the automobile engine, the management of the main shaft of the equipment for producing the engine is intelligently transformed and predictably maintained in the current automobile production and manufacturing process, namely, the collected data is processed, analyzed and summarized by collecting the daily maintenance data of the equipment, the replacement and shutdown faults of spare parts and the like, the summarized rule or experience is incorporated into a preventive maintenance plan for implementation, the traditional workshop predictive maintenance depends on the experience of a maintenance specialist, therefore, a maintenance service department of a client enterprise hopes to accurately predict the possible failure mode and the service life of the equipment in a workshop by means of a big data technology and an artificial intelligence technology, help to realize the diagnosis and early warning of the standby state and even the whole-period life management of the equipment, and guide the department to make a decision to reduce the downtime of the equipment, the emergency of workshop shutdown is avoided as much as possible, a main shaft predictive maintenance intelligent system is further added on a main shaft for a production engine, the using condition and the maintenance time of the main shaft can be predicted, the using data of the main shaft is collected through a sensor module 1 arranged on the main shaft, the intelligent maintenance is convenient, meanwhile, an information acquisition module 2 is used for receiving the information data from the sensor module 1 on the main shaft, an information comparison module 3 is used for receiving the information acquired by the information acquisition module 2, the information is calculated and compared through a comparison data model 33 constructed by a KAFKA algorithm to form a diagnosis result, an information decision module 4 is used for receiving the diagnosis result of the information comparison module 3, and then an optimized maintenance scheme is displayed through the comparison data, so that the current state and the service life of workshop equipment can be intelligently diagnosed and managed, the method is disclosed on a billboard in a chart visualization mode, helps to guide maintenance departments to daily maintain corresponding equipment and adjust production plans by production scheduling departments, and more importantly, reduces production halt risks of enterprises caused by equipment faults and improves the working efficiency of maintenance personnel.

Referring to fig. 3, the sensor module 1 includes a roundness sensor module 11, a rotation speed sensor module 12, acolor sensor module 13, a shape sensor module 14, asound sensor module 15, aposition sensor module 16, and a temperature sensor module 17, the roundness sensor module 11, the rotation speed sensor module 12, thesound sensor module 15, and the temperature sensor module 17 are disposed on the main shaft and used for collecting roundness data, rotation speed data, sound data, and temperature data during processing of the main shaft, and the positions of the main shaft in a factory are conveniently determined, positions of the main shaft in a factory are detected, and color and shape recognition of the positions are conveniently determined through thecolor sensor module 13, the shape sensor module 14, and theposition sensor module 16.

And then through, and then through adopting the sensor of multiple different grade type for the application data of the collection main shaft equipment of full aspect, conveniently make the predictive contrast of maintenance through calculation and contrast, and then be favorable to improving the accuracy of prediction.

Referring to fig. 2, theinformation collection module 2 includes avoice recognition module 21, anumber recognition module 22, ascene recognition module 23, anobject recognition module 24, acolor recognition module 25, and a datainformation recognition module 26, so as to recognize and receive data characteristics on the spindle sensor module 1, facilitate comparison of calculated data, and improve maintenance efficiency.

Furthermore, thescene recognition module 23, theobject recognition module 24 and thecolor recognition module 25 facilitate collecting the position and use information of the device, further comparing with the stored data in the database to determine the device as a specific spindle device, and then thevoice recognition module 21, thenumber recognition module 22 and the datainformation recognition module 26 facilitate collecting the use condition of the spindle device and making predictive maintenance.

Referring to fig. 4, theinformation comparison module 3 comprises the following steps:

s1, importing the main shaft information data acquired by theinformation acquisition module 2 into theinformation comparison module 3 by the data importing module 31;

s2, receiving data 32, receiving the main shaft information of the data import 31;

s3, the data model 33 calculates and compares the spindle information received to the data import 31 in the designed data model 33 to obtain a comparison value;

s4,data comparison 34, comparing the value calculated by the data model 33 with the stored value preset with the data;

and S5, data exporting 35, displaying the data comparison result exportinginformation comparison module 3, if the numerical value is the same as the preset value, displaying that maintenance is not needed, and if the numerical value is different from the preset value, displaying that maintenance is needed.

And then, through the data import 31, the data receiving 32, the data model 33, thedata comparison 34 and the data export 35, the data information of the spindle device collected by theinformation collection module 2 is compared through the data import 31, the data receiving 32, the data model 33, thedata comparison 34 and the data export 35 to diagnose the spindle device.

Referring to fig. 6, the data model 33 is constructed by the following steps:

s1, acquiring data 331, namely, acquiring data such as replacement and shutdown faults of spare parts by collecting daily maintenance data of the main shaft;

s2, analyzing data 332, and classifying, analyzing and summarizing the collected data;

s3,data modeling 333, summarizing different rules or experiences for different data types by analyzing different types of data, and constructing calculation models of different data types;

s4 anddata storage 334, wherein models constructed into different data types are stored, so that calling is facilitated;

and S5, calling 335 data, and calling calculation models of different data types according to different data types to facilitate information decision comparison.

Furthermore, the data acquisition 331 module collects data such as daily maintenance data of the main shaft, replacement of spare parts and shutdown faults, then, through data analysis 332, classification processing, analysis and summarization are performed on the collected data, anddata modeling 333 summarizes different rules or experiences of different data types through analyzing different types of data, so as to construct calculation models of different data types, meanwhile,data storage 334 is used for storing the models constructed into different data types in a database, so that calling is convenient, and data calling 335 is used for facilitating information decision comparison through calling the calculation models of different data types in the database according to different data types.

Referring to fig. 5, theinformation decision module 4 includes a user login 41, a data query 42, a data judgment 43, a database storage 44, and a data display 45, and theinformation decision module 4 can perform the data query 42 on the data derived by theinformation comparison module 3 after the user logs in 41, call the previous equipment daily maintenance data, spare part replacement shutdown faults, and the like of the database storage 44 when performing the information data query 42, and sum up the rules or experiences of the solution, then perform the data judgment 43 according to the query result, then store the processing data and the scheme in the database storage 44, facilitate the next comparison and call, then display the optimal solution through the data display 45, and facilitate the detection personnel to detect with reference to the detection scheme.

Furthermore, the system organization is displayed through a tree structure by setting user management, organization management, area management, menu management, role management, dictionary management and operation logs in a user login 41, so that the system organization is convenient to observe and adjust, meanwhile, the system menu operation authority is configured, role menu authority distribution such as button authority identification and the like, and the role is set to divide data range authority according to the organization, so that visual information display and function limitation are convenient for operators, a user can perform data query 42 on data derived from theinformation comparison module 3 after performing the user login 41, the conventional equipment daily maintenance data of a database storage 44 is called when performing the information data query 42, spare part replacement shutdown faults and the like, and summarized solved rules or experiences, then data judgment 43 is performed according to query results, and then the processed data and schemes are stored in the database storage 44, the next comparison and calling are facilitated, and then the optimal solution is displayed through the data display 45, so that the detection personnel can conveniently detect by referring to the detection scheme.

Referring to fig. 5, the user login 41 includes user management, organization management, area management, menu management, role management, dictionary management, and operation log, and is used to complete user configuration and develop role menu authority assignment and operation record query of company configuration and system city area model in a tree structure.

Furthermore, the system organization is displayed through the tree structure, observation and adjustment are convenient to carry out, meanwhile, role menu authority distribution such as system menu operation authority, button authority identification and the like is configured, and the role is set to carry out data range authority division according to the organization, so that visual information display and function limitation are convenient for operators.

Referring to fig. 1, an intelligent method for predictive maintenance of a main shaft of an automobile engine includes the following steps:

s1, the sensor module 1 is used for monitoring the main shaft for production and collecting the main shaft data to facilitate intelligent maintenance;

s2, theinformation acquisition module 2 is used for collecting information data from the sensor module 1 on the main shaft;

and S3, theinformation comparison module 3 is used for receiving the information collected by theinformation collection module 2 and comparing the information to form a diagnosis result.

And S4, theinformation decision module 4 is used for receiving the diagnosis result of theinformation comparison module 3 and then displaying the optimized maintenance scheme through the comparison data.

Furthermore, the current state and the service life of the workshop equipment can be intelligently diagnosed and managed, and the current state and the service life of the workshop equipment are disclosed on a billboard in a chart visualization mode, so that the method helps to guide a maintenance department to adjust the daily maintenance of the corresponding equipment and a production scheduling department to the production plan, and more importantly, the method reduces the production stop risk of an enterprise caused by equipment faults and improves the working efficiency of maintenance personnel.

Referring to fig. 6, theinformation comparison module 3 includes the steps of constructing the data model 33:

s1, acquiring data 331, namely, acquiring data such as replacement and shutdown faults of spare parts by collecting daily maintenance data of the main shaft;

s2, analyzing data 332, and classifying, analyzing and summarizing the collected data;

s3,data modeling 333, summarizing different rules or experiences for different data types by analyzing different types of data, and constructing calculation models of different data types;

s4 anddata storage 334, wherein models constructed into different data types are stored, so that calling is facilitated;

and S5, calling 335 data, and calling calculation models of different data types according to different data types to facilitate information decision comparison.

And then through the data acquisition 331 module, through collecting the data of daily maintenance of the main shaft, spare part change data such as the stoppage fault, then through data analysis 332, through classifying process, analysis summarize to the data collected, anddata modeling 333, summarize different rules or experience with different data types through analyzing different data types, thus establish the computational model of different data types,data storage 334, through constructing the model of different data types to store in the database, facilitate the transfer, data call 335, through transferring the computational model of different data types in the database according to different data types and facilitate the information decision-making comparison.

Referring to fig. 5, theinformation decision module 4 includes a user login 41, a data query 42, a data judgment 43, a database storage 44, and a data display 45, and theinformation decision module 4 can perform the data query 42 on the data derived by theinformation comparison module 3 after the user logs in 41, call the previous equipment daily maintenance data, spare part replacement shutdown faults, and the like of the database storage 44 when performing the information data query 42, and sum up the rules or experiences of the solution, then perform the data judgment 43 according to the query result, then store the processing data and the scheme in the database storage 44, facilitate the next comparison and call, then display the optimal solution through the data display 45, and facilitate the detection personnel to detect with reference to the detection scheme.

Furthermore, the current state and the service life of the workshop equipment can be intelligently diagnosed and managed, and the current state and the service life of the workshop equipment are disclosed on a billboard in a chart visualization mode, so that the method helps to guide a maintenance department to adjust the daily maintenance of the corresponding equipment and a production scheduling department to the production plan, and more importantly, the method reduces the production stop risk of an enterprise caused by equipment faults and improves the working efficiency of maintenance personnel.

The device obtained by the design can basically meet the requirement that the current state and the service life of workshop equipment can be intelligently diagnosed and managed, the device is disclosed on a billboard in a chart visualization mode to help a maintenance department to adjust the daily maintenance and production schedule of corresponding equipment, the service life of the equipment is improved by twenty percent after the system is on line, the cost of each engine main shaft is millions of yuan, the equipment replacement cost can be saved by about ten millions of yuan each year, more importantly, the shutdown risk of an enterprise caused by equipment failure is reduced, and the working efficiency of maintenance personnel is improved.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The utility model provides an intelligent system is maintained in automobile engine main shaft predictability, includes sensor module (1), information acquisition module (2), information contrast module (3) and information decision-making module (4), its characterized in that: the sensor module (1) is used for monitoring a main shaft for production, and collecting main shaft data to facilitate intelligent maintenance;

the information acquisition module (2) is used for collecting information data from the sensor module (1) on the main shaft;

the information comparison module (3) is used for receiving the information acquired by the information acquisition module (2) and comparing the information to form a diagnosis result.

The information decision module (4) is used for receiving the diagnosis result of the information comparison module (3) and then displaying an optimized maintenance scheme through comparison data.

2. The intelligent system for predictive maintenance of the main shaft of the automobile engine as claimed in claim 1, wherein the sensor module (1) comprises a roundness sensor module (11), a rotation speed sensor module (12), a color sensor module (13), a shape sensor module (14), a sound sensor module (15), a position sensor module (16) and a temperature sensor module (17), the roundness sensor module (11), the rotation speed sensor module (12), the sound sensor module (15) and the temperature sensor module (17) are arranged on the main shaft for collecting roundness data, rotation speed data, sound data and temperature data in the process of the main shaft, and the color sensor module (13), the shape sensor module (14) and the position sensor module (16) are used for facilitating the determination of the position in a factory, the detection of the position and the shape recognition, it is convenient to determine the location where maintenance is required.

3. The intelligent system for predictive maintenance of the main shaft of the automobile engine as claimed in claim 2, wherein the information acquisition module (2) comprises a voice recognition module (21), a number recognition module (22), a scene recognition module (23), an object recognition module (24), a color recognition module (25) and a data information recognition module (26), so as to recognize and receive data characteristics on the main shaft sensor module 1, facilitate comparison and calculation of data, and improve maintenance efficiency.

4. The intelligent system for predictive maintenance of the main shaft of the automobile engine as claimed in claim 3, wherein the information comparison module (3) comprises the following steps:

s1, data import (31), importing the main shaft information data collected by the information collection module (2) into the information comparison module (3);

s2, receiving data (32), and receiving the main shaft information of the data import (31);

s3, the data model (33) calculates and compares the spindle information received to the data import (31) in the designed data model (33) to obtain a comparison value;

s4, data comparison (34), comparing the value calculated by the data model (33) with the stored value preset with the data;

and S5, data derivation (35), displaying the data comparison result derived information comparison module (3), wherein if the numerical value is the same as the preset value, the data is displayed without maintenance, and if the numerical value is different from the preset value, the data is displayed with maintenance.

5. The intelligent system for predictive maintenance of automotive engine spindles as claimed in claim 4, wherein said data model (33) is constructed by the steps of:

s1, data acquisition (331), namely, data such as replacement and shutdown faults of spare parts are acquired by collecting daily maintenance data of the main shaft;

s2, analyzing data (332), and classifying, analyzing and summarizing the collected data;

s3, data modeling (333), summarizing different rules or experiences for different data types by analyzing different types of data, and then constructing a comparison data model 33 through a KAFKA algorithm, so that calculation models of different data types are constructed to facilitate comparison calculation;

s4, data storage (334) for storing models constructed into different data types, so as to facilitate calling;

and S5, calling data (335), and calling calculation models of different data types according to different data types to facilitate information decision comparison.

6. The automobile engine spindle predictive maintenance intelligent system according to claim 5, characterized in that the information decision module (4) comprises a user login (41), a data query (42), a data judgment (43), a database storage (44) and a data display (45), and the information decision module (4) can perform the data query (42) on the data derived by the information comparison module (3) after the user login (41), call the past equipment daily maintenance data, spare part replacement shutdown faults and the like of the database storage (44) when performing the information data query (42), and summarize the solved rules or experiences, then perform the data judgment (43) according to the query result, then store the processed data and the scheme in the database storage (44), facilitate the next comparison call, and then display the optimal solution through the data display (45), the detection personnel can conveniently refer to the detection scheme for detection.

7. The intelligent system for predictive maintenance of automobile engine spindles according to claim 6, characterized in that the user login (41) comprises user management, organization management, area management, menu management, role management, dictionary management and operation logs, which are used for completing user configuration and expanding the role menu authority assignment and operation record query of company configuration and system city area model in a tree structure.

8. The intelligent system for predictive maintenance of the main shaft of the automobile engine as claimed in claim 7, wherein an intelligent method for predictive maintenance of the main shaft of the automobile engine comprises the following steps:

s1, the sensor module (1) is used for monitoring the main shaft for production and collecting the main shaft data to facilitate intelligent maintenance;

s2, the information acquisition module (2) is used for collecting information data from the sensor module (1) on the main shaft;

and S3, the information comparison module (3) is used for receiving the information collected by the information collection module (2) and comparing the information to form a diagnosis result.

And S4, the information decision module (4) is used for receiving the diagnosis result of the information comparison module (3) and then displaying the optimized maintenance scheme through the comparison data.

9. The intelligent system for predictive maintenance of the main shaft of the automobile engine as claimed in claim 8, wherein the information comparison module (3) comprises a data model (33) construction step:

s1, data acquisition (331), namely, data such as replacement and shutdown faults of spare parts are acquired by collecting daily maintenance data of the main shaft;

s2, analyzing data (332), and classifying, analyzing and summarizing the collected data;

s3, data modeling (333), summarizing different rules or experiences for different data types by analyzing different types of data, and constructing calculation models of different data types;

s4, data storage (334) for storing models constructed into different data types, so as to facilitate calling;

and S5, calling data (335), and calling calculation models of different data types according to different data types to facilitate information decision comparison.

10. The automobile engine spindle predictive maintenance intelligent system according to claim 9, wherein the information decision module (4) comprises a user login (41), a data query (42), a data judgment (43), a database storage (44) and a data display (45), and the information decision module (4) can perform the data query (42) on the data derived by the information comparison module (3) after the user login (41) is performed by the user, call the past equipment daily maintenance data, spare part replacement shutdown faults and the like of the database storage (44) when the information data query (42) is performed, and summarize the solved rules or experiences, then perform the data judgment (43) according to the query result, then store the processed data and the scheme in the database storage (44), facilitate the next comparison call, and then display the optimal solution through the data display (45), the detection personnel can conveniently refer to the detection scheme for detection.

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