CN111240300A - Vehicle health state evaluation model construction method based on big data - Google Patents
Vehicle health state evaluation model construction method based on big data Download PDFInfo
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- CN111240300A CN111240300A CN202010012162.2A CN202010012162A CN111240300A CN 111240300 A CN111240300 A CN 111240300A CN 202010012162 A CN202010012162 A CN 202010012162A CN 111240300 A CN111240300 A CN 111240300A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Abstract
The invention discloses a vehicle health state evaluation model construction method based on big data, which is used for monitoring vehicle states from multiple aspects, analyzing and processing the data by collecting main data sources of a vehicle, including vehicle-mounted TCMS data, online monitoring data of a running gear, online detection data of a wheel set pantograph, detection data of other process equipment and the like, diagnosing and analyzing faults of the vehicle by integrating various dynamic and static information of the vehicle through big data multi-model analysis, classifying health states of subsystems of a vehicle system, enhancing cognitive depth of product structure and performance through big data technical research on a train key system, and realizing a vehicle system health state prediction method and a health state evaluation realization scheme by combining a vehicle health state diagnosis model theory.
Description
Technical Field
The invention belongs to the field of maintenance management of rail transit vehicles, and particularly relates to a vehicle health state evaluation model construction method based on big data.
Background
With the rapid development of Chinese economy and the continuous construction of cities, more and more people are gathered in cities, and the total amount of travel is greatly increased. As a public transportation mode with large transportation volume and rapidness, the urban rail transit greatly relieves the traffic pressure and is rapidly developed in recent years. However, with the rapid development of rail transit, the operation intensity of rail transit vehicles is increasing, and various sudden problems occur in subway vehicles during high-intensity overload operation, so that higher requirements are met in the aspect of rail transit operation management.
For the vehicle health state of a vehicle base, namely the current vehicle maintenance state, the vehicle health state is mainly in the 'planned repair' level, namely, a corresponding repair process is developed according to a repair rule and an operation history, and in consideration of factors such as economic benefit, repair efficiency and the like, the future development trend is to adopt more economic and scientific 'state repair'. The establishment of the vehicle health state evaluation model becomes the key point of the development of the rail transit industry by acquiring complete and timely state parameters.
At present, a large number of devices are contained in an intelligent vehicle base, and the faced reality is as follows: the electric passenger car, the trackside equipment and the electromechanical equipment adopt a large amount of electronic information equipment and a production control system to generate mass data in real time, but because the electric passenger car has a plurality of professions and various professional technologies are complex, an advanced technical means is required to be utilized to extract useful information from the mass data, promote and guide the evaluation of the actual equipment state and the optimization of a maintenance strategy, and improve the management level of equipment maintenance in an intelligent vehicle base.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems, the invention provides a vehicle health state evaluation model construction method based on big data, and the vehicle state analysis and trend analysis are realized.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: a vehicle health state evaluation model building method based on big data comprises the following steps:
(1) acquiring state monitoring data of each subsystem of the vehicle;
(2) carrying out data preprocessing and analysis;
(3) classifying the health state of each subsystem;
(4) constructing different health state evaluation models for each subsystem;
(5) evaluating and predicting the health state of the multi-model subsystem;
(6) and giving a maintenance scheme based on the health state evaluation result.
Further, in the step 1, the vehicle subsystem includes an on-board system, a running gear system, and a wheel-set pantograph system.
Further, in the step 3, the vehicle fault classification includes vehicle, component location, system, fault type and occurrence time.
Further, in step 3, the health status classification includes health, sub-health, light fault, medium fault, and serious fault.
Further, in the step 4, a dynamic weight adaptive evaluation model is established by using logistic regression, TOPIS multiple objective and an entropy weight method.
Further, in the step 2, each subsystem controller is a data acquisition device, and performs preprocessing and analysis on the acquired data for operation control of the subsystem.
Further, in step 5, the ground control center receives the state monitoring data of each subsystem, evaluates and predicts the state of each subsystem based on different health state evaluation models, and warns the failure of the subsystem.
Has the advantages that: according to the invention, the maintenance of the vehicle base is promoted from 'plan maintenance' to 'state maintenance' by establishing the vehicle health state evaluation model, so that the maintenance efficiency of the vehicle base is improved; and the health state of the vehicle system is predicted and alarmed through data accumulation and analysis of the vehicle base, so that an auxiliary production decision is achieved, and the overhaul cost is saved.
Drawings
FIG. 1 is a diagram of a vehicle data warning model;
FIG. 2 is a multi-dimensional map of vehicle component fault classification;
FIG. 3 is a schematic view of a vehicle health diagnostic;
fig. 4 is a schematic diagram of a health status assessment scheme.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The vehicle health state evaluation model based on big data is constructed, and support of multi-source monitoring data is comprehensively utilized to realize support of 'state correction' of a vehicle base.
The method comprises the steps of automatically collecting vehicle state data, calculating evaluation indexes, constructing a vehicle health state evaluation model from multiple dimensions such as vehicle reliability, key component detection parameters, service life prediction and defect hidden danger, establishing a dynamic weight self-adaptive evaluation model by adopting algorithms such as logistic regression, TOPIS multi-objective, entropy weight method and the like, and providing a basis for optimizing maintenance procedures and strategies, optimizing maintenance plan scheduling and replacing components.
The invention relates to a vehicle health state evaluation model construction method based on big data, which comprises the following specific steps:
(1) acquiring multi-source vehicle state monitoring data;
in order to ensure the full-automatic safe operation of the vehicle, the vehicle state is monitored from a plurality of aspects, and the main data sources of the vehicle comprise: the vehicle-mounted TCMS data, the online monitoring data of the running gear, the online detection data of the wheel set pantograph, the detection data of other process equipment and the like are shown in Table 1.
TABLE 1
| Serial number | Uploaded data | Remarks for |
| 1 | Traction assistance system data | Traction assistance command, status, fault |
| 2 | Brake system data | Brake system command, status, |
| 3 | Passenger information system data | Passenger information system commands, status, faults |
| 4 | Door system data | Door status, failure |
| 5 | Air conditioning system data | Air conditioner command, status, |
| 6 | Battery management system data | Accumulator state, fault |
| 7 | Battery charger data | State and fault of accumulator charger |
| 8 | Fire alarmSystem data | Fire order, status, failure |
| 9 | Public broadcast system data | Common broadcast command, status, fault |
| 10 | Walking part detection system | Running gear detection state and fault |
| 11 | Bow net detection system | Bow net detecting system fault |
The vehicle data early warning model is shown in fig. 1, and maintenance suggestions are provided by performing operation monitoring, fault diagnosis, fault prediction and health assessment on data.
(2) Carrying out data preprocessing and analysis;
the data analysis and processing can be divided into two layers, the first layer is data acquisition equipment, each subsystem controller is data acquisition equipment, and is also the first layer analysis and processing equipment of data, carries out simple analysis and processing to the data acquisition, is used for the operation control of subsystem.
(3) Classifying the health state of each subsystem;
through the big data technology research that goes on train key subsystem, the reinforcing is to the cognitive degree of depth of product structure and performance, uses big data technology to carry out the analysis to magnanimity operation and maintenance data simultaneously, combines the actual demand of overhauing, solves the potential safety hazard problem that exists from the source, better provides information guarantee for the train application, reinforcing train safety risk prevention and control level.
For fault classification of vehicle components, multi-dimensional data analysis is considered, and as shown in fig. 2, faults are classified through six dimensions of vehicles, components, component positioning, systems, fault types and occurrence time.
Through intelligent acquisition and big data multi-model analysis, various dynamic and static information of the vehicle are integrated, and the fault of the vehicle is diagnosed and analyzed, which mainly comprises the following contents: (1) describing the fault; (2) a fault code; (3) a fault triggering condition; (4) a failure vanishing condition; (5) influence on the system; (6) classifying fault grades; (7) driver solutions; (8) the maintenance solution.
For example, the health status classifications of the running gear and the wheel rail are shown in table 2.
TABLE 2
| Serial number | Object | Health grade | |
| 1 | Bearing assembly | Healthy, sub-healthy, minor, moderate, major failure | |
| 2 | Bearing temperature | Medium fault, |
|
| 3 | Tread surface | Healthy, sub-healthy, minor, moderate, major failure | |
| 4 | Track | Health, sub-health, failure |
(4) Constructing different health state diagnosis models for each subsystem;
(4.1) in the vehicle-mounted system, carrying out model and big data analysis on key components of the traction system according to the mapping relation between the characteristic parameters and the performance of the key components and test data under various working conditions; the health diagnosis model of the air conditioner and the vehicle door system is mainly a data model which is built for judging and predicting the health state of a subsystem by reasonably utilizing the existing data and applying various reasonable reasoning algorithms on the basis of the acquired data; the brake control system should have a maintenance interface through which the portable and movable test device can read fault and status information of the brake control system.
And (4.2) according to the generalized resonance fault diagnosis technology, the generalized resonance signals are extracted by installing a sensor to receive vibration and impact generalized resonance, and then the generalized resonance signals are demodulated, so that all harmless vibration spectrums are eliminated, and harmful impact spectrums are highlighted.
(4.3) in the wheel set pantograph system, the wheel contour diameter, the tread abrasion, the rim thickness, the sliding plate abrasion and other detection indexes are detected, the sudden faults at least comprise defects, the basic data comprise factors such as traveling mileage and the like, comprehensive evaluation is carried out, the evaluation result is influenced by a plurality of factors, and each influence factor occupies different weights. And obtaining the weight by combining an objective weighting method of an entropy weight method and a variable weight formula according to the individual score of each influence factor to obtain the overall health index.
As shown in FIG. 3, the big data platform processes and analyzes real-time data, vehicle-mounted data and environmental data to build a health evaluation model and provide final maintenance suggestions.
(5) Predicting the health state of the multi-model subsystem;
the second layer of data analysis and processing is a ground control center, the ground control center receives the subsystem state transmitted by each train, and the states of the subsystems are predicted and diagnosed based on a big data theory, so that the intelligent diagnosis of the subsystem states is realized, the faults of the subsystems are early warned in advance, and maintenance suggestions are pushed in time.
The ground large data center is adopted to analyze the ground vehicle-mounted data in real time, and fault early warning of influencing driving safety, driving order and riding experience of a walking part, a braking system, a traction system, an air conditioning system and the like is realized. And by combining the actual influence degree on the train operation, reasonable health assessment dimensionality is constructed by utilizing big data analysis and mining technology, vehicle health state assessment is constructed, and support is provided for train operation safety state assessment and management.
The walking part carries out early warning on parts such as an axle box, a gear box, a traction motor and the like through the change trend of the axle temperature; the traction system carries out early warning of network pressure fluctuation, overcurrent, a transmission system (a coupling, a gear box) and the like by monitoring key parameters of the traction system; the air conditioner carries out air conditioner abnormity early warning through monitoring; and the vehicle door carries out big data statistics and service life and fault prediction by recording data.
(6) Providing a maintenance scheme based on the health state evaluation result;
the health state evaluation scheme combines sample data acquired by a related science and technology vehicle-mounted fault diagnosis system and a trackside detection system, and simultaneously considers factors such as equipment reliability, maintenance economy, maintenance difficulty and the like, comprehensively calculates the health index of a diagnosis object, and accordingly realizes the evaluation of the health state. The health status evaluation scheme is shown in fig. 4, data is fused into the health scheme through extraction of various information, and finally corresponding health grade and maintenance suggestions are given.
The invention utilizes the field acquisition data information of the vehicle equipment to establish an informatization system based on maintenance full-life state tracking and management mechanism, realizes the daily operation, daily maintenance and other management of equipment such as electric buses, engineering vehicles, vehicle processes and the like, improves the production efficiency, and can output and count results, monitor and trace the process, and predict and prevent faults.
Claims (7)
1. A vehicle health state evaluation model building method based on big data is characterized by comprising the following steps:
(1) acquiring state monitoring data of each subsystem of the vehicle;
(2) carrying out data preprocessing and analysis;
(3) classifying the health state of each subsystem;
(4) constructing different health state evaluation models for each subsystem;
(5) evaluating and predicting the health state of the multi-model subsystem;
(6) and giving a maintenance scheme based on the health state evaluation result.
2. The big data-based vehicle health assessment model building method according to claim 1, wherein in step 1, the vehicle subsystems comprise an on-board system, a running gear system and a wheel-set pantograph system.
3. The big-data-based vehicle health assessment model building method according to claim 1, wherein in said step 3, vehicle fault classification comprises vehicle, component location, system, fault type, occurrence time.
4. The big-data-based vehicle health status assessment model construction method according to claim 1, wherein in said step 3, the health status classification comprises healthy, sub-healthy, light fault, medium fault, and severe fault.
5. The big data-based vehicle health status assessment model construction method according to claim 1, wherein in said step 4, a dynamic weight adaptive assessment model is established by using logistic regression, TOPIS multiple objective and entropy weight method.
6. The big-data-based vehicle health status assessment model building method according to claim 1, wherein in the step 2, each subsystem controller is a data acquisition device, and performs preprocessing and analysis on the acquired data for operation control of the subsystem.
7. The method for constructing the vehicle health state assessment model based on big data as claimed in claim 1, wherein in step 5, the ground control center receives the state monitoring data of each subsystem, and assesses and predicts the state of each subsystem based on different health state assessment models to warn of subsystem failure.
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| CN111806516A (en) * | 2020-06-30 | 2020-10-23 | 苏州华启智能科技有限公司 | Health management device and method for intelligent train monitoring and operation and maintenance |
| CN112188436A (en) * | 2020-09-28 | 2021-01-05 | 四川紫荆花开智能网联汽车科技有限公司 | Vehicle-mounted unit monitoring system and method based on V2X communication |
| CN112491952A (en) * | 2020-10-21 | 2021-03-12 | 武汉虹信科技发展有限责任公司 | Automatic train maintenance control system and control method |
| CN112580153A (en) * | 2020-12-29 | 2021-03-30 | 成都运达科技股份有限公司 | Health state management system and method for vehicle running gear monitoring component |
| CN113361858A (en) * | 2021-05-10 | 2021-09-07 | 上海工程技术大学 | Vehicle state evaluation method and system based on rail transit vehicle fault data |
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| CN113859306A (en) * | 2020-06-30 | 2021-12-31 | 株洲中车时代电气股份有限公司 | Locomotive data expert diagnostic analysis method, device and system |
| CN112188436A (en) * | 2020-09-28 | 2021-01-05 | 四川紫荆花开智能网联汽车科技有限公司 | Vehicle-mounted unit monitoring system and method based on V2X communication |
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| CN114118470A (en) * | 2021-11-25 | 2022-03-01 | 中铁二院工程集团有限责任公司 | Intelligent management and control method and system for production and operation of full-automatic driving vehicle base |
| CN114118470B (en) * | 2021-11-25 | 2023-09-05 | 中铁二院工程集团有限责任公司 | Intelligent control method and system for production operation of full-automatic driving vehicle base |
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| CN114248818A (en) * | 2022-01-13 | 2022-03-29 | 南京融才交通科技研究院有限公司 | Intelligent information transportation supervision method and system based on rail transit |
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| CN115214700A (en) * | 2022-05-26 | 2022-10-21 | 广州汽车集团股份有限公司 | Vehicle health management method and system |
| CN115465339A (en) * | 2022-10-08 | 2022-12-13 | 南京融才交通科技研究院有限公司 | Communication system for ground rail transit and train control method |
| CN115952923A (en) * | 2023-03-03 | 2023-04-11 | 国能铁路装备有限责任公司 | Inspection method and device for improving transport efficiency of railway wagon |
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