CN114861830A - Automatic fault diagnosis method for vehicle-mounted equipment of subway signal system - Google Patents
Automatic fault diagnosis method for vehicle-mounted equipment of subway signal system Download PDFInfo
- Publication number
- CN114861830A CN114861830A CN202210639117.9A CN202210639117A CN114861830A CN 114861830 A CN114861830 A CN 114861830A CN 202210639117 A CN202210639117 A CN 202210639117A CN 114861830 A CN114861830 A CN 114861830A
- Authority
- CN
- China
- Prior art keywords
- fault
- equipment
- information
- diagnosis
- signal system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 54
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000012545 processing Methods 0.000 claims abstract description 23
- 238000012423 maintenance Methods 0.000 claims abstract description 19
- 238000010295 mobile communication Methods 0.000 claims abstract description 9
- 230000008569 process Effects 0.000 claims abstract description 4
- 238000007476 Maximum Likelihood Methods 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 230000002159 abnormal effect Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 5
- 230000011664 signaling Effects 0.000 description 3
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/29—Graphical models, e.g. Bayesian networks
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L15/00—Indicators provided on the vehicle or train for signalling purposes
- B61L15/0081—On-board diagnosis or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/043—Distributed expert systems; Blackboards
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Evolutionary Biology (AREA)
- Mechanical Engineering (AREA)
- Computational Linguistics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention discloses a fault automatic diagnosis method for vehicle-mounted equipment of a subway signal system, which comprises the following steps that (1) a data acquisition device acquires equipment state information in real time and transmits the equipment information to a maintenance center through a mobile communication facility; (2) the maintenance center analyzes the equipment information received from the mobile communication facility and carries out real-time data display, storage record and fault diagnosis on the equipment information; (3) sending the fault diagnosis result to maintenance personnel mobile equipment; (4) the maintenance personnel process the fault by means of a fault processing manual and experience according to the diagnosis information or the early warning information; (5) storing a fault processing result database; compared with the prior art, the method has the advantages of automatic diagnosis, accurate fault location, timely early warning and alarming when the working state of the signal equipment is monitored to be abnormal, and the like, and improves the fault diagnosis accuracy and the fault processing efficiency of the vehicle-mounted signal equipment.
Description
Technical Field
The invention relates to the technical field of fault diagnosis of vehicle-mounted signal equipment, in particular to a fault automatic diagnosis method of vehicle-mounted equipment of a subway signal system.
Background
The devices involved in ATP and ATO on the train are called on-board signaling devices. The vehicle-mounted signal system is a key component of a rail transit signal system and is responsible for the operation control of the train, and the fault of the vehicle-mounted signal system has great influence on the safe operation and the transportation efficiency of the train. The subway has long running time and high strength, and the aging and the failure of the equipment cannot be avoided. At present, the following problems exist in the maintenance and fault diagnosis of subway vehicle-mounted signal equipment:
first, failure handling is inefficient. The downloading, recording, counting and summarizing of the fault information are completed manually, and the processing efficiency is low. The fault maintenance method is generally to perform maintenance based on the fault performance and further referring to past experience.
Secondly, the fault data records are not standardized and incomplete. The fault maintenance record format is not standardized, the fault record emphasizes the accident result, and the fault cause is not recorded too much, so that the fault record lacks rigor.
On the other hand, fault diagnosis based on expert knowledge has certain one-sidedness. The accuracy depends on the size of the knowledge base and its accuracy, and there are large differences in the diagnostic rules given by different experts.
Finally, the replacement of the vehicle-mounted signal equipment generally refers to an equipment maintenance periodic table issued by a higher level to replace the equipment. And for some equipment which is frequently damaged and fails, the corresponding life cycle is not predicted and estimated, and the equipment is replaced by experience only, and the key equipment is comprehensively checked every few days. Although the safety of the pen-related information can be ensured by the maintenance means, a large amount of manpower and material resources are consumed.
In summary, vehicle signaling device failures have complexity and uncertainty, and diagnostic systems are still in need of improvement. In order to solve the problems, the invention provides a method for automatically diagnosing the fault of the vehicle-mounted equipment of the subway signal system, which can be used for timely providing fault information and early warning information by acquiring fault data in real time and diagnosing the fault, thereby improving the diagnosis accuracy and the diagnosis efficiency.
Disclosure of Invention
In order to achieve the purpose, the invention provides the following technical scheme: a fault automatic diagnosis method for vehicle-mounted equipment of a subway signal system comprises the following steps:
s1, the data acquisition device acquires the equipment state information in real time and transmits the equipment information to the maintenance center through the mobile communication facility;
s2, the maintenance center analyzes the equipment information received from the mobile communication facility and carries out real-time data display, storage record and fault diagnosis on the equipment information;
s3: sending the fault diagnosis result to maintenance personnel mobile equipment;
s4: the maintenance personnel process the fault by means of a fault processing manual and experience according to the diagnosis information or the early warning information;
s5: storing a fault processing result database;
s6: and according to the fault processing result database, performing structure learning by adopting a K2 algorithm and performing parameter learning by adopting a maximum likelihood estimation algorithm to establish an optimal fault diagnosis model.
As a preferred technical means of the present invention, in step S1, status information of the ATO and ATP is collected through two sets of interfaces.
As a preferred technical aspect of the present invention, in step S2, diagnostic information or warning information is generated using a failure diagnosis model to realize failure diagnosis of the signal vehicle-mounted device.
As a preferred technical means of the present invention, in step S5, the failure processing result database is composed of complete failure data and failure handling means.
As a preferred embodiment of the present invention, the establishing of the optimal fault diagnosis model in step S6 includes the steps of:
a, determining network node information, namely a fault processing result database, by analyzing fault record information of a maintenance center; the network nodes are divided into 3 levels of fault phenomena, fault positions and fault reasons; determining the dependency relationship among the nodes by combining a fault processing result database, and establishing a Bayesian network based on expert knowledge;
b, taking a proper amount of data from the historical fault data as sample data;
c, performing structure learning and parameter learning by using the sample data;
and d, carrying out appropriate reduction treatment on the learned network structure to obtain an optimal fault diagnosis model which is more in line with practical problems.
As a preferred technical mode of the present invention, the failure phenomenon is a phenomenon after a hardware module causing a signal failure in an ATO and ATP system fails; the fault positions are an ATP system and an ATO system; the cause of the failure is a hardware module that causes signal failure in the ATO and ATP systems.
As a preferred technical aspect of the present invention, the failure diagnosis method estimates the probability of each node of the failure cause using the failure phenomenon as an input.
As a preferable technical mode of the invention, the early warning information method is to calculate the probability of each node of a fault part layer by taking a fault phenomenon as input.
Compared with the prior art, the invention has the following beneficial effects:
the automatic fault diagnosis method for the vehicle-mounted equipment of the subway signal system realizes real-time monitoring of the fault state of the vehicle-mounted equipment of the signal, and the constructed fault diagnosis model realizes early warning and diagnosis of faults before and after the occurrence of the faults and timely transmits fault information to maintenance personnel, thereby playing an important role in diagnosis efficiency and fault processing efficiency.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a step diagram of a method for automatically diagnosing a fault of an on-board device of a subway signaling system according to the present invention;
FIG. 2 is a diagram of the collector structure of the present invention;
FIG. 3 is a schematic diagram of the Bayesian network structure of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
As shown in fig. 1, the present invention is a method for automatically diagnosing a fault of a vehicle-mounted device of a subway signal system, which comprises the following steps:
and 3, step 3: sending the fault diagnosis result in the step 2 to maintenance personnel mobile equipment;
and 4, step 4: the maintenance personnel timely process the fault according to the diagnosis information or the early warning information by means of a fault processing manual and experience;
and 5: after the fault is processed, a fault processing result database is stored;
step 6: according to the fault processing result database, performing structure learning by adopting a K2 algorithm and parameter learning by adopting a maximum likelihood estimation algorithm to establish an optimal fault diagnosis model (Bayesian network) as shown in FIG. 3, wherein the Bayesian network is a structural schematic diagram of the invention, and the diagram only schematically shows the fault diagnosis model structure of the invention for the sake of concise drawing, and is not regarded as an actual structure;
the establishment of the model comprises the following steps:
a, determining network node information, namely a fault processing result database, by analyzing fault record information of a maintenance center; the network nodes are divided into 3 levels of fault phenomena, fault positions and fault reasons; determining the dependency relationship among the nodes by combining a fault processing result database, and establishing a Bayesian network based on expert knowledge;
b, taking a proper amount of data from the historical fault data as sample data;
c, performing structure learning and parameter learning by using sample data;
d, carrying out appropriate reduction processing on the learned network structure to obtain an optimal fault diagnosis model which is more in line with practical problems;
example of fault diagnosis:
fifteen lines Luxiu railway station goes upward 15001# car FAM mode and can't be converted, and ATO can't the motor car, and collection system gathers behind the fault data, sends to the maintenance center in real time and carries out fault diagnosis, and diagnosis information is generated through the diagnosis of fault diagnosis model, and the fault diagnosis result is locked and is: FAM unavailability room is caused by driver key not turning to position, ATO is unavailable because button press time is too short; and transmitting the obtained fault diagnosis result to related maintenance personnel mobile equipment in time, and carrying out maintenance after the train returns to the warehouse.
Failure early warning example:
the ten-wire control central station 101# vehicle network port connection is lost, the acquisition device acquires fault data and then sends the fault data to the maintenance center in real time to carry out fault diagnosis, and early warning information is generated through fault diagnosis model diagnosis, wherein the warning reason is that (1) the port is enabled but the network cable or the optical fiber of the port is not connected; (2) a network or fiber failure; (3) sending a partial fault to the port; (4) the local terminal receives a partial failure. The obtained fault early warning information is timely transmitted to the mobile equipment of the relevant maintenance personnel, and after the train returns to the warehouse, the maintenance personnel maintain and repair the train according to the information, so that the possibility of major faults is avoided.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the scope of the present invention is not limited to the embodiments. Therefore, various modifications may be made to the present invention within the scope of the claims and their equivalents.
While there have been shown and described what are at present considered the fundamental principles and essential features of the invention and its advantages, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art will be able to refer to the description as a whole, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (8)
1. The automatic fault diagnosis method for the vehicle-mounted equipment of the subway signal system is characterized by comprising the following steps of:
s1, the data acquisition device acquires the equipment state information in real time and transmits the equipment information to the maintenance center through the mobile communication facility;
s2, the maintenance center analyzes the equipment information received from the mobile communication facility and carries out real-time data display, storage record and fault diagnosis on the equipment information;
s3: sending the fault diagnosis result to maintenance personnel mobile equipment;
s4: the maintenance personnel process the fault by means of a fault processing manual and experience according to the diagnosis information or the early warning information;
s5: storing a fault processing result database;
s6: and according to the fault processing result database, performing structure learning by adopting a K2 algorithm and performing parameter learning by adopting a maximum likelihood estimation algorithm to establish an optimal fault diagnosis model.
2. The method for automatically diagnosing the fault of the equipment on the train of the subway signal system as claimed in claim 1, wherein in step S1, the status information of the automatic operation system ATO of the train on the control side and the automatic protection system ATP of the train on the control side is collected through two sets of interfaces.
3. The method for automatically diagnosing the fault of the on-board equipment of the subway signal system as claimed in claim 1, wherein in step S2, the fault diagnosis model is used to generate the diagnosis information or the early warning information to diagnose the fault of the on-board equipment.
4. The method for automatically diagnosing the fault of the equipment carried in the subway signal system as claimed in claim 1, wherein in step S5, the fault processing result database is composed of complete fault data and fault handling means.
5. The method for automatically diagnosing the fault of the equipment on board the subway signal system as claimed in claim 1, wherein in step S6, the establishment of the optimal fault diagnosis model comprises the following steps:
a, determining network node information, namely a fault processing result database, by analyzing fault record information of a maintenance center; the network nodes are divided into 3 levels of fault phenomena, fault positions and fault reasons; determining the dependency relationship among the nodes by combining a fault processing result database, and establishing a Bayesian network based on expert knowledge;
b, taking a proper amount of data from the historical fault data as sample data;
c, performing structure learning and parameter learning by using the sample data;
and d, carrying out appropriate reduction treatment on the learned network structure to obtain an optimal fault diagnosis model which is more in line with practical problems.
6. The method for automatically diagnosing the fault of the vehicle-mounted equipment of the subway signal system as claimed in claim 1, wherein the fault phenomenon is a phenomenon after a hardware module causing signal fault in an ATO and ATP system is in fault; the fault positions are an ATP system and an ATO system; the cause of the failure is a hardware module that causes signal failure in the ATO and ATP systems.
7. The method for automatically diagnosing the fault of the equipment on board the subway signal system as claimed in claim 3, wherein the fault diagnosis method is to calculate the probability of each node of the fault reason by using the fault phenomenon as input.
8. The method for automatically diagnosing the fault of the equipment on board the subway signal system as claimed in claim 3, wherein the early warning information method is to calculate the probability of each node of the fault location layer by taking the fault phenomenon as input.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210639117.9A CN114861830A (en) | 2022-06-07 | 2022-06-07 | Automatic fault diagnosis method for vehicle-mounted equipment of subway signal system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210639117.9A CN114861830A (en) | 2022-06-07 | 2022-06-07 | Automatic fault diagnosis method for vehicle-mounted equipment of subway signal system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114861830A true CN114861830A (en) | 2022-08-05 |
Family
ID=82623617
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210639117.9A Pending CN114861830A (en) | 2022-06-07 | 2022-06-07 | Automatic fault diagnosis method for vehicle-mounted equipment of subway signal system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114861830A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116308286A (en) * | 2023-03-23 | 2023-06-23 | 江苏工鼎工业技术有限公司 | Rail transit self-diagnosis type door machine system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109656228A (en) * | 2018-12-04 | 2019-04-19 | 江苏大学 | A kind of subway signal system onboard equipment fault automatic diagnosis method |
-
2022
- 2022-06-07 CN CN202210639117.9A patent/CN114861830A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109656228A (en) * | 2018-12-04 | 2019-04-19 | 江苏大学 | A kind of subway signal system onboard equipment fault automatic diagnosis method |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116308286A (en) * | 2023-03-23 | 2023-06-23 | 江苏工鼎工业技术有限公司 | Rail transit self-diagnosis type door machine system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108444727A (en) | Vehicular remote monitoring method, monitoring device and monitoring system | |
| US6810312B2 (en) | Method for identifying a loss of utilization of mobile assets | |
| CN111489071A (en) | Maintenance method and system for rail transit vehicle | |
| CN109067491B (en) | Train broadcast audio monitoring method, platform and system | |
| CN112559488A (en) | Escalator full life cycle data management method and system based on data center station | |
| CN114861830A (en) | Automatic fault diagnosis method for vehicle-mounted equipment of subway signal system | |
| CN113128721A (en) | Intelligent operation and maintenance management system and method for charging pile | |
| CN108599989B (en) | MVB bus detection method and device | |
| MXPA04007888A (en) | Cab signal quality detecting and reporting system and method. | |
| CN107991965A (en) | Standardization electricity business failure emergency command management method based on intellectual monitoring | |
| CN115848463A (en) | Intelligent operation and maintenance system and method | |
| CN115329598A (en) | Data processing platform based on digital twins | |
| CN109625025B (en) | BTM equipment early warning system | |
| CN108667664A (en) | Automatic O＆M monitoring and based reminding method, block chain and business platform | |
| CN111367263A (en) | Mine car monitoring device and system | |
| CN106896799B (en) | A kind of locomotive inspection and repair management method | |
| CN110058575A (en) | A kind of process units driving and parking management system | |
| CN213397654U (en) | Fault diagnosis and maintenance equipment for high-speed rail braking system | |
| CN211857276U (en) | Mine car monitoring device and system | |
| CN113371039A (en) | Urban rail train inspection work learning system and implementation platform thereof | |
| CN107933622A (en) | A kind of real-time emergency commading system of abnormal train running | |
| CN115903720A (en) | Fault diagnosis system and method for rail transit, and storage medium | |
| CN115017174A (en) | Display content monitoring method and system of passenger information system and electronic equipment | |
| RU136780U1 (en) | MOBILE MONITORING SYSTEM | |
| CN117009105B (en) | Method for pre-alarming state of subway vehicle-mounted equipment based on storm flow calculation in real time |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |