CN112758132A - End-to-end time delay monitoring method and device for locomotive PHM system - Google Patents
End-to-end time delay monitoring method and device for locomotive PHM system Download PDFInfo
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- CN112758132A CN112758132A CN202110028214.XA CN202110028214A CN112758132A CN 112758132 A CN112758132 A CN 112758132A CN 202110028214 A CN202110028214 A CN 202110028214A CN 112758132 A CN112758132 A CN 112758132A
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- 230000003137 locomotive effect Effects 0.000 title claims abstract description 59
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000012544 monitoring process Methods 0.000 title claims abstract description 18
- 238000012545 processing Methods 0.000 claims abstract description 18
- 238000003745 diagnosis Methods 0.000 claims abstract description 15
- 230000005540 biological transmission Effects 0.000 claims abstract description 10
- 238000005070 sampling Methods 0.000 claims description 25
- 238000005096 rolling process Methods 0.000 claims description 7
- 238000012800 visualization Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 3
- 238000007726 management method Methods 0.000 description 11
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L15/00—Indicators provided on the vehicle or vehicle train for signalling purposes ; On-board control or communication systems
- B61L15/0081—On-board diagnosis or maintenance
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L15/00—Indicators provided on the vehicle or vehicle train for signalling purposes ; On-board control or communication systems
- B61L15/0072—On-board train data handling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2477—Temporal data queries
Abstract
The invention relates to an end-to-end time delay monitoring method for a locomotive PHM system, which specifically comprises the following steps: s101, a vehicle-mounted acquisition system acquires locomotive vehicle state data, and adds a state data acquisition timestamp in the locomotive vehicle state data; s102, respectively counting time delay from the data acquisition time of the locomotive vehicle state data to the subsystem by a plurality of PHM subsystems positioned on an end-to-end transmission path and processing time delay of the locomotive vehicle state data in the subsystem to generate time delay statistical data; s103, each PHM subsystem outputs time delay statistical data to a time delay management module of the locomotive PHM system, and the time delay management module conducts end-to-end time delay diagnosis of the locomotive PHM system and time delay diagnosis in each PHM subsystem according to the time delay statistical data of each PHM subsystem. Compared with the prior art, the method has the advantages of reducing the data volume in the PHM system, simplifying the complexity of monitoring the end-to-end time delay of the system and the like.
Description
Technical Field
The invention relates to the field of railway transportation, in particular to a method and a device for monitoring end-to-end time delay of a PHM (locomotive PHM) system.
Background
The end-to-end time delay is a key system performance index of the PHM system of the locomotive vehicle, namely, the time delay is experienced between the time when the PHM system collects the state parameter data of the locomotive vehicle from the vehicle-mounted part of the PHM system and the time when the PHM system shows the judgment of the fault according to the state parameter data of the locomotive vehicle. The end-to-end delay usually passes through the delay of each subsystem such as a vehicle-mounted subsystem, a network transmission subsystem, a big data subsystem, an application subsystem, a visualization subsystem and the like.
In a conventional end-to-end time delay monitoring method, each subsystem stamps respective time stamps on the vehicle state parameter data, and monitors time delay through the time stamps. However, for a mass data system such as a PHM system of a locomotive vehicle, time stamping is performed on each subsystem, which causes an unnecessarily large expansion of the system data storage amount. Therefore, a new method and a new device for monitoring the end-to-end delay of the PHM system of the rolling stock are needed to solve the problem of monitoring the end-to-end delay of the PHM system of the rolling stock.
Disclosure of Invention
The invention aims to provide a method and a device for monitoring end-to-end time delay of a PHM system of a locomotive vehicle in order to overcome the defect that the storage capacity of system data is unnecessarily and greatly expanded due to the mass data of the PHM system of the locomotive vehicle in a subsystem time stamp adding mode in the prior art.
The purpose of the invention can be realized by the following technical scheme:
an end-to-end time delay monitoring method for a locomotive PHM system specifically comprises the following steps:
s101, a vehicle-mounted acquisition system acquires locomotive vehicle state data, and adds a state data acquisition timestamp in the locomotive vehicle state data;
s102, respectively counting time delay from the data acquisition time of the locomotive vehicle state data to the subsystem by a plurality of PHM subsystems positioned on an end-to-end transmission path and processing time delay of the locomotive vehicle state data in the subsystem to generate time delay statistical data;
s103, each PHM subsystem outputs the time delay statistical data to a time delay management module of the locomotive PHM system, and the time delay management module makes end-to-end time delay diagnosis of the locomotive PHM system and time delay diagnosis in each PHM subsystem according to the time delay statistical data of each PHM subsystem.
The PHM subsystems in step S102 are all the PHM subsystems with processing delay in the locomotive PHM system.
Further, the types of the PHM subsystem include a network transmission subsystem, a big data subsystem, an application subsystem and a visualization subsystem.
The PHM subsystem calculates the processing time delay in the subsystem by adopting a statistical sampling method.
Furthermore, a plurality of PHM subsystems are statistically sampled by adopting a consistent statistical sampling rule, or each PHM subsystem is statistically sampled by adopting an independent statistical sampling rule.
An apparatus for using the end-to-end time delay monitoring method of the locomotive vehicle PHM system, comprising a memory and a processor, the memory including an on-board acquisition section and a subsystem section, the processor including a system time delay management section, the method being stored in the memory in the form of a computer program and executed by the processor, and when executed, performing the steps of:
s201, the vehicle-mounted acquisition part acquires locomotive vehicle state data, and adds a state data acquisition timestamp in the locomotive vehicle state data;
s202, the subsystem part counts time delay from the data acquisition time of the locomotive vehicle state data to the subsystem part and the processing time delay of the locomotive vehicle state data in the subsystem part to generate time delay statistical data;
and S203, the sub-system part outputs the time delay statistical data to the system time delay management part, and the system time delay management part makes end-to-end time delay diagnosis of the PHM system of the locomotive vehicle and time delay diagnosis of a plurality of control modules in the sub-system part according to the time delay statistical data of the sub-system part.
Further, the subsystems comprise a network transmission subsystem, a big data subsystem, an application subsystem and a visualization subsystem.
The subsystem calculates the processing time delay in the subsystem by adopting a statistical sampling method.
Furthermore, a plurality of the subsystems carry out statistical sampling by adopting a consistent statistical sampling rule, or each subsystem carries out statistical sampling by adopting an independent statistical sampling rule.
Compared with the prior art, the invention has the following beneficial effects:
1. the method avoids that each subsystem of the PHM system of the rolling stock attaches the time stamp to the rolling stock state data, minimizes the data attachment of the time stamp and reduces the data volume in the PHM system.
2. The invention supports each subsystem of the PHM system of the locomotive vehicle to count the processing time delay of each link in an independent and distributed mode, and greatly simplifies the complexity of end-to-end time delay monitoring of the system.
3. The invention utilizes the big data characteristic of the PHM system of the locomotive vehicle, counts the sampling data by summarizing the independent time delay of each subsystem, does not require that each subsystem must track the same data packet to calculate the time delay, and more efficiently realizes the integral end-to-end time delay monitoring and time delay diagnosis of the PHM system of the locomotive vehicle.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a flow chart of the operation of an apparatus using the method of the present invention.
Reference numerals:
1-vehicle-mounted acquisition part; 2-a subsystem part; and 3, a system delay management part.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
As shown in fig. 1, an end-to-end delay monitoring method for a PHM system of a rolling stock specifically includes the following steps:
s101, a vehicle-mounted acquisition system acquires locomotive vehicle state data, and adds a state data acquisition timestamp in the locomotive vehicle state data;
s102, respectively counting time delay from the data acquisition time of the locomotive vehicle state data to the subsystem by a plurality of PHM subsystems positioned on an end-to-end transmission path and processing time delay of the locomotive vehicle state data in the subsystem to generate time delay statistical data;
s103, each PHM subsystem outputs time delay statistical data to a time delay management module of the locomotive PHM system, and the time delay management module conducts end-to-end time delay diagnosis of the locomotive PHM system and time delay diagnosis in each PHM subsystem according to the time delay statistical data of each PHM subsystem.
The PHM subsystems in step S102 are all the PHM subsystems with processing delay in the locomotive PHM system.
Types of PHM subsystems include network transport subsystems, big data subsystems, application subsystems, and visualization subsystems.
The PHM subsystem calculates the processing delay in the subsystem by adopting a statistical sampling method.
Statistical sampling is carried out among the plurality of PHM subsystems by adopting a consistent statistical sampling rule, or each PHM subsystem carries out statistical sampling by adopting an independent statistical sampling rule.
As shown in fig. 2, an apparatus for monitoring an end-to-end delay using a PHM system of a rolling stock includes a memory and a processor, the memory includes an on-board acquisition part 1 and a subsystem part 2, the processor includes a system delay management part 3, and the method is stored in the memory in the form of a computer program and executed by the processor, and when executed, the following steps are implemented:
s201, a vehicle-mounted acquisition part 1 acquires locomotive vehicle state data and adds a state data acquisition timestamp to the locomotive vehicle state data;
s202, the subsystem part 2 counts time delay from the data acquisition time of the locomotive state data to the subsystem part 2 and processing time delay in the locomotive state data subsystem to generate time delay statistical data;
s203, the subsystem part 2 outputs the time delay statistical data to the system time delay management part 3, and the system time delay management part 3 makes end-to-end time delay diagnosis of the PHM system of the locomotive vehicle and time delay diagnosis in the subsystem part according to the time delay statistical data of a plurality of subsystems in the subsystem part 2.
The types of subsystems include a network transmission subsystem, a big data subsystem, an application subsystem, and a visualization subsystem.
The subsystem calculates the processing delay in the subsystem by using a statistical sampling method.
The plurality of subsystems carry out statistical sampling by adopting a consistent statistical sampling rule, or each subsystem carries out statistical sampling by adopting an independent statistical sampling rule.
In addition, it should be noted that the specific embodiments described in the present specification may have different names, and the above descriptions in the present specification are only illustrations of the structures of the present invention. Minor or simple variations in the structure, features and principles of the present invention are included within the scope of the present invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.
Claims (10)
1. An end-to-end time delay monitoring method for a PHM system of a locomotive vehicle is characterized by comprising the following steps:
s101, a vehicle-mounted acquisition system acquires locomotive vehicle state data, and adds a state data acquisition timestamp in the locomotive vehicle state data;
s102, respectively counting time delay from the data acquisition time of the locomotive vehicle state data to the subsystem by a plurality of PHM subsystems positioned on an end-to-end transmission path and processing time delay of the locomotive vehicle state data in the subsystem to generate time delay statistical data;
s103, each PHM subsystem outputs the time delay statistical data to a time delay management module of the locomotive PHM system, and the time delay management module makes end-to-end time delay diagnosis of the locomotive PHM system and time delay diagnosis in each PHM subsystem according to the time delay statistical data of each PHM subsystem.
2. The method of claim 1, wherein the PHM subsystems of step S102 are all PHM subsystems with processing delay in the PHM system of the rolling stock.
3. The method of claim 2, wherein the PHM subsystems are of the type including a network transmission subsystem, a big data subsystem, an application subsystem, and a visualization subsystem.
4. The method of claim 1, wherein the PHM subsystem calculates the processing delay within the subsystem using statistical sampling.
5. The method of claim 4, wherein a plurality of PHM subsystems are statistically sampled using a consistent statistical sampling rule, or each PHM subsystem is statistically sampled using an independent statistical sampling rule.
6. An apparatus for end-to-end delay monitoring method using a locomotive PHM system as claimed in claim 1, characterized by comprising a memory and a processor, said memory comprising an on-board acquisition part (1) and a subsystem part (2), said processor comprising a system delay management part (3), said method being stored in the memory in the form of a computer program and being executed by the processor, and when executed performing the following steps:
s201, the vehicle-mounted acquisition part (1) acquires locomotive vehicle state data, and adds a state data acquisition timestamp to the locomotive vehicle state data;
s202, the subsystem part (2) counts time delay from the data acquisition time of the locomotive vehicle state data to the subsystem part (2) and processing time delay of the locomotive vehicle state data in the subsystem to generate time delay statistical data;
s203, the sub-system part (2) outputs time delay statistical data to the system time delay management part (3), and the system time delay management part (3) conducts end-to-end time delay diagnosis of the PHM system of the locomotive vehicle and time delay diagnosis of the sub-system part according to the time delay statistical data of the sub-system part (2).
7. An apparatus for end-to-end delay monitoring using a PHM system of a locomotive vehicle according to claim 6, wherein the subsystem part (2) in step S202 is a subsystem with processing delay.
8. The apparatus of claim 7, wherein said subsystems comprise a network transmission subsystem, a big data subsystem, an application subsystem and a visualization subsystem.
9. The apparatus of claim 6 wherein the subsystem calculates the processing delay within the subsystem using statistical sampling.
10. The apparatus of claim 9, wherein a plurality of said subsystems are statistically sampled using a consistent statistical sampling rule, or each subsystem is statistically sampled using an independent statistical sampling rule.
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| CN110758414A (en) * | 2019-11-11 | 2020-02-07 | 上海电气集团股份有限公司 | Rail transit vehicle control system and control method |
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- 2021-01-08 CN CN202110028214.XA patent/CN112758132A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103332211A (en) * | 2013-05-21 | 2013-10-02 | 北京交通大学 | Improvement method of performance of train operation control system |
| US20150302163A1 (en) * | 2014-04-17 | 2015-10-22 | Lockheed Martin Corporation | Prognostics and health management system |
| CN109948169A (en) * | 2017-12-20 | 2019-06-28 | 中国中车股份有限公司 | A kind of railway freight-car prognostic and health management system |
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