CN110889511A - Intelligent health management system for rail transit - Google Patents
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- CN110889511A CN110889511A CN201810928220.9A CN201810928220A CN110889511A CN 110889511 A CN110889511 A CN 110889511A CN 201810928220 A CN201810928220 A CN 201810928220A CN 110889511 A CN110889511 A CN 110889511A
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- 230000036541 health Effects 0.000 title claims abstract description 17
- 238000001514 detection method Methods 0.000 claims abstract description 90
- 238000012423 maintenance Methods 0.000 claims abstract description 44
- 238000012545 processing Methods 0.000 claims abstract description 34
- 230000008439 repair process Effects 0.000 claims abstract description 21
- 238000004458 analytical method Methods 0.000 claims description 37
- 229910000831 Steel Inorganic materials 0.000 claims description 30
- 239000010959 steel Substances 0.000 claims description 30
- 238000003745 diagnosis Methods 0.000 claims description 29
- 238000007405 data analysis Methods 0.000 claims description 15
- 239000004519 grease Substances 0.000 claims description 13
- 238000005507 spraying Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 6
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 3
- 238000005299 abrasion Methods 0.000 claims description 3
- 229910052799 carbon Inorganic materials 0.000 claims description 3
- 239000007921 spray Substances 0.000 claims description 3
- 206010039203 Road traffic accident Diseases 0.000 abstract description 2
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Abstract
The rail transit intelligent health management system comprises a rail network detection and data acquisition subsystem, a vehicle equipment and wheel pair detection subsystem, a cloud big data processing subsystem and an operation and maintenance guidance subsystem. The invention automatically tracks the states of the track and the equipment and carries out early warning on possible accidents; the occurrence frequency of traffic accidents is effectively reduced, and the operation technical level and the management level are improved; the transition from event-dominated repair (i.e., post-mortem repair) and time-dependent repair (i.e., scheduled repair) to state-based repair strategies greatly reduces the operational and maintenance costs; the intelligent traffic and intelligent traffic support platform is provided for the construction of the intelligent city. Passenger riding experience is guaranteed, and urban rail transit service level is improved.
Description
Technical Field
The invention belongs to the field of rail transit health guarantee, and particularly relates to an intelligent rail transit health management system.
Background
In recent years, research on optimization of train maintenance strategies has been increasingly focused, wherein fault prediction and health management techniques and state-based maintenance are the focus of current research. With the aid of advanced sensor technology, a large amount of operating data of complex equipment can be collected, transmitted and stored, and the use of such data for guiding the maintenance of the equipment and for making advanced maintenance strategies is an important research content of current maintenance work.
The maintenance mode of the high-speed rail in China is changed from the traditional 'planned maintenance' to 'state maintenance'. When data prove that the equipment is likely to break down, the equipment is maintained in time, so that the maintenance efficiency is improved, the maintenance cost is saved, and the safety and reliability of the equipment are improved. The maintenance strategy is formulated and improved, the fault time point of the equipment needs to be accurately grasped, and the health state evaluation is carried out, so that the fault prediction is the core research content of the optimization of the train maintenance strategy.
In the rail transit industry, foreign companies such as Siemens, Ricardo and Kenolr establish a safety state diagnosis and prediction system, and play a key role in improving the operation reliability of rail transit, improving the maintenance efficiency and reducing the maintenance cost. With the rapid development of high-speed rail industry in China, the number of trains in on-line operation increases year by year, and the quality of the trains such as design, production, application, maintenance and the like is highly valued by the whole industry. How to improve the safety and reliability of rail transit operation, reduce operation and maintenance cost and improve service quality becomes an important subject of sustainable development of rail transit industry in China.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to solve the problems in the prior art of maintenance and repair of railways, tramways and subways, and improves the service lives of urban rail lines, vehicles and contact lines, ensures the operation safety of the lines and the riding comfort of passengers, improves the operation efficiency, saves the maintenance cost, reduces the maintenance time and prolongs the service life by systematically constructing technical innovations and application innovations such as comprehensive intelligent detection, expert diagnostic system analysis and guidance, cloud data service, Beidou + GIS positioning, guidance of rail restorative polishing, wheel set economic turning repair, grease spraying and wear reducing maintenance and the like.
In order to achieve the purpose, the rail transit intelligent health management system is characterized by comprising a rail network detection and data acquisition subsystem, a vehicle equipment and wheel pair detection subsystem, a cloud big data processing subsystem and an operation and maintenance guidance subsystem;
the rail network detection and data acquisition subsystem receives and analyzes rail network detection data obtained from the detection equipment, carries out trend analysis on the current detection data and uploads the current detection data to the cloud big data processing subsystem;
the vehicle equipment and wheel pair detection subsystem receives and analyzes data of shaft temperature, wheel pair size, wheel pair cracks, pantograph central line deviation and carbon sliding plate abrasion obtained from the vehicle equipment and wheel pair detection equipment, and uploads the current detection data to the cloud big data processing subsystem after trend analysis;
the cloud big data processing subsystem stores detection and trend analysis data of the rail network detection and data acquisition subsystem, the vehicle equipment and wheel set detection subsystem, analyzes and models the detection data through an algorithm, and outputs a line state and a diagnosis result.
The operation and maintenance guidance subsystem guides the worn steel rail to polish, guide the repair of the sprayed grease of the steel rail, guide the repair of the vehicle equipment and the economic turning of the wheel pair by utilizing the data of the rail network detection and data acquisition subsystem, the vehicle equipment and wheel pair detection subsystem and the cloud large data processing subsystem.
Preferably, the rail network detection and data acquisition subsystem comprises a rail network detection data analysis and diagnosis module, a differential GNSS positioning data module, a rail network state historical trend analysis module and a first data uploading module;
the rail network detection data analysis and diagnosis module scans the outline of the steel rail through the high-speed structured light sensor, compensates errors generated by vibration of a mechanical structure through the inertial navigation system, and detects parameters of the outline, the track gauge, the horizontal super-elevation, the triangular pit and the turnout of the rail;
the differential GNSS positioning data module adopts a differential GNSS and a speed encoder to position a sensor data acquisition place, and binds the sensor data with the position of the track parameter so as to facilitate data analysis;
the rail network state historical trend analysis module is used for comparing rail network data detected in different periods in the past and showing the change trend of the rail network state;
the first data uploading module uploads the local rail network detection data and the trend analysis data to the cloud big data processing subsystem.
Preferably, the vehicle equipment and wheel set detection subsystem comprises a vehicle and wheel set detection data analysis and diagnosis module, a vehicle equipment and wheel set state historical trend analysis module and a second data uploading module;
the vehicle and wheel pair detection data analysis and diagnosis module scans wheel pair and rail profile data through a high-precision 2D laser sensor, measures the axle temperature through an infrared sensor, measures vibration between a wheel pair and a steel rail through a vibration sensor to analyze cracks generated by long-time running of the wheel rail, performs preliminary analysis on collected data, and stores the data in a local database;
the vehicle equipment and wheel set state historical trend analysis module is used for comparing vehicle equipment and wheel set state data detected in different periods in a local database and displaying the change trend of the vehicle and wheel set states;
and the second data uploading module uploads the local vehicle equipment, wheel set state data and trend analysis data to the cloud big data processing subsystem.
Preferably, the cloud big data processing subsystem comprises a cloud database module, an expert diagnosis module and a GIS display module;
the cloud database module is used for storing original data and preliminary analysis data of rail network detection, vehicle equipment and wheel pair detection;
the expert diagnosis module learns expert analysis experience through an intelligent algorithm, analyzes and models detection data, and finally generates an expert diagnosis result through continuous learning and iteration to provide guidance for rail transit health maintenance;
and the GIS display module is used for displaying the line state and the expert diagnosis result by combining a line map and providing a cross-platform access interface.
Preferably, the operation and maintenance guidance subsystem comprises a steel rail grinding module, a steel rail grease spraying module, a vehicle equipment maintenance and wheel set turning module;
the steel rail grinding module guides the worn steel rail to grind by utilizing the data of the rail network detection and data acquisition subsystem and the cloud big data processing subsystem;
the steel rail grease spraying module guides the steel rail to spray grease for repair by utilizing the data of the rail network detection and data acquisition subsystem and the cloud big data processing subsystem;
and the vehicle equipment maintenance and wheel pair turning module guides the vehicle equipment maintenance and the wheel pair economic turning by utilizing the data of the vehicle equipment, the wheel pair detection subsystem and the cloud large data processing subsystem.
The invention has the beneficial effects that: (1) the states of the track and the equipment are automatically tracked through expert experience provided by an expert diagnosis system, and possible accidents are early warned in advance. Effectively reduce the frequency of occurrence of traffic accidents and improve the operation technical level and the management level.
(2) The transition from event-dominated repair (i.e., post-mortem repair) and time-dependent repair (i.e., scheduled repair) to state-based repair strategies greatly reduces the operational and maintenance costs.
(3) The intelligent traffic and intelligent traffic support platform is provided for the construction of the intelligent city.
(4) Passenger riding experience is guaranteed, and urban rail transit service level is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a block diagram of the architecture of the present invention
Detailed Description
The solution according to the invention, including the preferred solutions, will be described in further detail below by way of fig. 1 exemplifying some alternative embodiments of the invention. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without any inventive step, are within the scope of the present invention.
As shown in fig. 1, the rail transit intelligent health management system designed by the invention comprises a rail network detection and data acquisition subsystem, a vehicle equipment and wheel pair detection subsystem, a cloud big data processing subsystem and an operation and maintenance guidance subsystem.
The rail network detection and data acquisition subsystem receives and analyzes rail network detection data obtained from the detection equipment, carries out trend analysis on the current detection data and uploads the current detection data to the cloud big data processing subsystem;
preferably, the rail network detection and data acquisition subsystem comprises a rail network detection data analysis and diagnosis module, a differential GNSS positioning data module, a rail network state historical trend analysis module and a first data uploading module;
the rail network detection data analysis and diagnosis module scans the outline of the steel rail through the high-speed structured light sensor, compensates errors generated by vibration of a mechanical structure through the inertial navigation system, and detects parameters of the outline, the track gauge, the horizontal super-elevation, the triangular pit and the turnout of the rail;
the differential GNSS positioning data module adopts a differential GNSS and a speed encoder to position a sensor data acquisition place, and binds the sensor data with the position of the track parameter so as to facilitate data analysis;
the rail network state historical trend analysis module is used for comparing rail network data detected in different periods in the past and showing the change trend of the rail network state;
the first data uploading module uploads the local rail network detection data and the trend analysis data to the cloud big data processing subsystem.
The vehicle equipment and wheel pair detection subsystem receives and analyzes data of shaft temperature, wheel pair size, wheel pair cracks, pantograph central line deviation and carbon sliding plate abrasion obtained from the vehicle equipment and wheel pair detection equipment, and uploads the current detection data to the cloud big data processing subsystem after trend analysis;
preferably, the vehicle equipment and wheel set detection subsystem comprises a vehicle and wheel set detection data analysis and diagnosis module, a vehicle equipment and wheel set state historical trend analysis module and a second data uploading module;
the vehicle and wheel pair detection data analysis and diagnosis module scans wheel pair and rail profile data through a high-precision 2D laser sensor, measures the axle temperature through an infrared sensor, measures vibration between a wheel pair and a steel rail through a vibration sensor to analyze cracks generated by long-time running of the wheel rail, performs preliminary analysis on collected data, and stores the data in a local database;
the vehicle equipment and wheel set state historical trend analysis module is used for comparing vehicle equipment and wheel set state data detected in different periods in a local database and displaying the change trend of the vehicle and wheel set states;
and the second data uploading module uploads the local vehicle equipment, wheel set state data and trend analysis data to the cloud big data processing subsystem.
The cloud big data processing subsystem stores detection and trend analysis data of the rail network detection and data acquisition subsystem, the vehicle equipment and wheel set detection subsystem, analyzes and models the detection data through an algorithm, and outputs a line state and a diagnosis result.
Preferably, the cloud big data processing subsystem comprises a cloud database module, an expert diagnosis module and a GIS display module;
the cloud database module is used for storing original data and preliminary analysis data of rail network detection, vehicle equipment and wheel pair detection;
the expert diagnosis module learns expert analysis experience through an intelligent algorithm, analyzes and models detection data, and finally generates an expert diagnosis result through continuous learning and iteration to provide guidance for rail transit health maintenance;
and the GIS display module is used for displaying the line state and the expert diagnosis result by combining a line map and providing a cross-platform access interface.
The operation and maintenance guidance subsystem guides the worn steel rail to polish, guide the repair of the sprayed grease of the steel rail, guide the repair of the vehicle equipment and the economic turning of the wheel pair by utilizing the data of the rail network detection and data acquisition subsystem, the vehicle equipment and wheel pair detection subsystem and the cloud large data processing subsystem.
Preferably, the operation and maintenance guidance subsystem comprises a steel rail grinding module, a steel rail grease spraying module, a vehicle equipment maintenance and wheel set turning module;
the steel rail grinding module guides the worn steel rail to grind by utilizing the data of the rail network detection and data acquisition subsystem and the cloud big data processing subsystem;
the steel rail grease spraying module guides the steel rail to spray grease for repair by utilizing the data of the rail network detection and data acquisition subsystem and the cloud big data processing subsystem;
and the vehicle equipment maintenance and wheel pair turning module guides the vehicle equipment maintenance and the wheel pair economic turning by utilizing the data of the vehicle equipment, the wheel pair detection subsystem and the cloud large data processing subsystem.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and any modification, combination, replacement, or improvement made within the spirit and principle of the present invention is included in the scope of the present invention.
Claims (5)
1. The utility model provides a track traffic intelligence health management system which characterized in that: the system comprises a rail network detection and data acquisition subsystem, a vehicle equipment and wheel pair detection subsystem, a cloud big data processing subsystem and an operation and maintenance guidance subsystem;
the rail network detection and data acquisition subsystem receives and analyzes rail network detection data obtained from the detection equipment, carries out trend analysis on the current detection data and uploads the current detection data to the cloud big data processing subsystem;
the vehicle equipment and wheel pair detection subsystem receives and analyzes data of shaft temperature, wheel pair size, wheel pair cracks, pantograph central line deviation and carbon sliding plate abrasion obtained from the vehicle equipment and wheel pair detection equipment, and uploads the current detection data to the cloud big data processing subsystem after trend analysis;
the cloud big data processing subsystem stores detection and trend analysis data of the rail network detection and data acquisition subsystem, the vehicle equipment and wheel set detection subsystem, analyzes and models the detection data through an algorithm, and outputs a line state and a diagnosis result.
The operation and maintenance guidance subsystem guides the worn steel rail to polish, guide the repair of the sprayed grease of the steel rail, guide the repair of the vehicle equipment and the economic turning of the wheel pair by utilizing the data of the rail network detection and data acquisition subsystem, the vehicle equipment and wheel pair detection subsystem and the cloud large data processing subsystem.
2. The rail transit intelligent health management system of claim 1, wherein: the rail network detection and data acquisition subsystem comprises a rail network detection data analysis and diagnosis module, a differential GNSS positioning data module, a rail network state historical trend analysis module and a first data uploading module;
the rail network detection data analysis and diagnosis module scans the outline of the steel rail through the high-speed structured light sensor, compensates errors generated by vibration of a mechanical structure through the inertial navigation system, and detects parameters of the outline, the track gauge, the horizontal super-elevation, the triangular pit and the turnout of the rail;
the differential GNSS positioning data module adopts a differential GNSS and a speed encoder to position a sensor data acquisition place, and binds the sensor data with the position of the track parameter so as to facilitate data analysis;
the rail network state historical trend analysis module is used for comparing rail network data detected in different periods in the past and showing the change trend of the rail network state;
the first data uploading module uploads the local rail network detection data and the trend analysis data to the cloud big data processing subsystem.
3. The rail transit intelligent health management system of claim 1, wherein: the vehicle equipment and wheel set detection subsystem comprises a vehicle and wheel set detection data analysis and diagnosis module, a vehicle equipment and wheel set state historical trend analysis module and a second data uploading module;
the vehicle and wheel pair detection data analysis and diagnosis module scans wheel pair and rail profile data through a high-precision 2D laser sensor, measures the axle temperature through an infrared sensor, measures vibration between a wheel pair and a steel rail through a vibration sensor to analyze cracks generated by long-time running of the wheel rail, performs preliminary analysis on collected data, and stores the data in a local database;
the vehicle equipment and wheel set state historical trend analysis module is used for comparing vehicle equipment and wheel set state data detected in different periods in a local database and displaying the change trend of the vehicle and wheel set states;
and the second data uploading module uploads the local vehicle equipment, wheel set state data and trend analysis data to the cloud big data processing subsystem.
4. The rail transit intelligent health management system of claim 1, wherein: the cloud big data processing subsystem comprises a cloud database module, an expert diagnosis module and a GIS display module;
the cloud database module is used for storing original data and preliminary analysis data of rail network detection, vehicle equipment and wheel pair detection;
the expert diagnosis module learns expert analysis experience through an intelligent algorithm, analyzes and models detection data, and finally generates an expert diagnosis result through continuous learning and iteration to provide guidance for rail transit health maintenance;
and the GIS display module is used for displaying the line state and the expert diagnosis result by combining a line map and providing a cross-platform access interface.
5. The rail transit intelligent health management system of claim 1, wherein: the operation and maintenance guidance subsystem comprises a steel rail grinding module, a steel rail grease spraying module, a vehicle equipment maintenance and wheel set turning module;
the steel rail grinding module guides the worn steel rail to grind by utilizing the data of the rail network detection and data acquisition subsystem and the cloud big data processing subsystem;
the steel rail grease spraying module guides the steel rail to spray grease for repair by utilizing the data of the rail network detection and data acquisition subsystem and the cloud big data processing subsystem;
and the vehicle equipment maintenance and wheel pair turning module guides the vehicle equipment maintenance and the wheel pair economic turning by utilizing the data of the vehicle equipment, the wheel pair detection subsystem and the cloud large data processing subsystem.
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| CN201810928220.9A CN110889511A (en) | 2018-08-15 | 2018-08-15 | Intelligent health management system for rail transit |
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| CN201810928220.9A CN110889511A (en) | 2018-08-15 | 2018-08-15 | Intelligent health management system for rail transit |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112033335A (en) * | 2020-11-05 | 2020-12-04 | 成都中轨轨道设备有限公司 | Intelligent monitoring and early warning system and method for railway gauging rule |
| CN113988326A (en) * | 2021-10-09 | 2022-01-28 | 南京理工大学 | Subway equipment maintenance optimization method and system |
| CN115249387A (en) * | 2021-12-16 | 2022-10-28 | 北京九州一轨环境科技股份有限公司 | Intelligent rail transit monitoring and management system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103472344A (en) * | 2013-10-09 | 2013-12-25 | 株洲高新技术产业开发区壹星科技有限公司 | Railway vehicle servicing work comprehensive intelligent detecting system and detecting method |
| CN105574593A (en) * | 2015-12-18 | 2016-05-11 | 中南大学 | Track state static-state detection and control system and method based on cloud computing and big data |
-
2018
- 2018-08-15 CN CN201810928220.9A patent/CN110889511A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103472344A (en) * | 2013-10-09 | 2013-12-25 | 株洲高新技术产业开发区壹星科技有限公司 | Railway vehicle servicing work comprehensive intelligent detecting system and detecting method |
| CN105574593A (en) * | 2015-12-18 | 2016-05-11 | 中南大学 | Track state static-state detection and control system and method based on cloud computing and big data |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112033335A (en) * | 2020-11-05 | 2020-12-04 | 成都中轨轨道设备有限公司 | Intelligent monitoring and early warning system and method for railway gauging rule |
| CN113988326A (en) * | 2021-10-09 | 2022-01-28 | 南京理工大学 | Subway equipment maintenance optimization method and system |
| CN115249387A (en) * | 2021-12-16 | 2022-10-28 | 北京九州一轨环境科技股份有限公司 | Intelligent rail transit monitoring and management system |
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