State detection method and system for rail transit equipment

Abstract

The invention provides a state detection method and system for rail transit equipment, and relates to the technical field of traffic equipment.The method comprises the following steps that real-time operation data, image data, operation and maintenance data and life expectancy data of the rail transit equipment within a preset time period are collected; determining residual life data of the rail transit equipment according to the real-time operation data, the image data, the operation maintenance data and the expected life data; comparing the residual life data with a plurality of preset thresholds, and judging the running state of the rail transit equipment; according to the invention, fault prediction can be carried out according to the related data of the rail transit equipment, equipment maintenance preparation can be made in advance in a more targeted manner, and compared with maintenance after equipment fault, the shutdown time can be shortened, the operation efficiency can be improved, and the waste of maintenance resources can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of transportation equipment, and more particularly to a state detection method and system for rail transportation equipment. Background Art

[0002] At present, rail transit equipment is an indispensable part of the rail transit system. They are crucial to ensuring the safe operation of rail transit, improving passenger experience and ensuring system reliability. Through continuous data monitoring and analysis of the operating status of rail transit equipment, rail transit operators can promptly identify potential problems, take measures to prevent failures, and ensure passenger safety and stable system operation.

[0003] However, in the existing status detection methods for rail transit equipment, rail transit equipment can only be repaired within a predetermined planned time, or when the rail transit equipment fails, and it is impossible to perform pre-maintenance according to the situation of the rail transit equipment in advance. At the same time, due to the large number of rail transit equipment, there are some parts that do not need to be repaired but are also inspected, or many parts have failed before the maintenance time, which increases the probability of equipment failure on the one hand, and wastes resources on the other hand.

[0004] Therefore, how to provide a state detection method for rail transit equipment that can solve the above problems is an issue that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In view of this, the present invention provides a state detection method and system for rail transit equipment, which can predict faults based on relevant data of the rail transit equipment, and make more targeted equipment maintenance preparations in advance. Compared with repairing equipment after a failure, it can shorten downtime, improve operational efficiency, and reduce waste of maintenance resources.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A method for detecting the state of rail transit equipment comprises the following steps:

[0008] Collect real-time operation data, image data, operation and maintenance data, and life expectancy data of rail transit equipment within a preset period;

[0009] Determine the remaining life data of the rail transit equipment according to the real-time operation data, image data, operation and maintenance data, and expected life data;

[0010] Compare the remaining life data with multiple preset thresholds to determine the operating status of rail transit equipment.

[0011] Preferably, the specific process of determining the operating status of the rail transit equipment includes:

[0012] When the remaining life data is less than or equal to a first preset threshold, the rail transit equipment is classified and the corresponding rail transit equipment is replaced according to the importance.

[0013] Preferably, the specific process of determining the operating status of the rail transit equipment further includes:

[0014] When the remaining life data is greater than a first preset threshold, a fault prediction is performed on the rail transit equipment to determine a corresponding fault prediction result.

[0015] Preferably, the specific processing process of determining the corresponding fault prediction result includes:

[0016] When the remaining life data is greater than a first preset threshold and less than or equal to a second preset threshold, constructing an equipment failure analysis model;

[0017] Input the real-time operation data, the image data, the operation and maintenance data, and the remaining life data into the equipment failure analysis model to perform failure prediction and obtain corresponding failure prediction results;

[0018] Preventive maintenance is performed on the rail transit equipment according to the fault prediction result.

[0019] Preferably, the specific process of determining the operating status of the rail transit equipment further includes:

[0020] When the remaining life data is greater than a second preset threshold, the real-time operation data and image data of the rail transit equipment continue to be monitored.

[0021] Preferably, the specific process of determining the remaining life data of rail transit equipment includes:

[0022] The operation and maintenance data are divided to obtain corresponding positive operation index data and negative operation index data, and the real-time operation data and the image data are divided to determine corresponding positive data and negative data;

[0023] Determining the weights of the positive operation indicator data and the negative operation indicator data, and the weights of the positive data and the negative data;

[0024] A regression model is constructed, and the positive operation index data and the negative operation index data, the positive data and the negative data are normalized, and the remaining life data is obtained by combining the corresponding weights.

[0025] The present invention also provides a state detection system for rail transit equipment, comprising:

[0026] A collection module is used to collect real-time operation data, image data, operation and maintenance data, and life expectancy data of rail transit equipment within a preset period of time;

[0027] A calculation module, used to determine the remaining life data of the rail transit equipment according to the real-time operation data, image data, operation and maintenance data and expected life data;

[0028] The judgment module is used to compare the remaining life data with multiple preset thresholds to judge the operating status of the rail transit equipment.

[0029] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for detecting the state of rail transit equipment, which has the following beneficial effects:

[0030] (1) The present invention can timely discover potential faults of equipment and handle them in advance by real-time monitoring and analyzing relevant data of the operating status of rail transit equipment, thereby improving the reliability and stability of the equipment;

[0031] (2) Reduce maintenance costs: Through preventive maintenance, the downtime and maintenance costs caused by sudden equipment failures can be avoided;

[0032] (3) Optimize resource allocation: Rationally arrange maintenance plans and resource allocation based on the remaining life and operating status of the equipment to improve resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0034] Figure 1 An overall flow chart of a state detection method for rail transit equipment provided by the present invention;

[0035] Figure 2 A structural principle block diagram of a state detection system for rail transit equipment provided by the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] See also Figure 1 As shown, an embodiment of the present invention discloses a state detection method for rail transit equipment, comprising the following steps:

[0038] Collecting real-time operation data, image data, operation and maintenance data, and life expectancy data of rail transit equipment within a preset period of time, wherein the real-time operation data of rail transit equipment may include electrical data, vibration data, environmental data, energy consumption data, speed data, and communication status data, and the operation and maintenance data may include operation records, maintenance records, and fault records. Rail transit equipment may specifically include trains, environmental control systems, and signal systems;

[0039] Determine the remaining life data of rail transit equipment based on real-time operation data, image data, operation and maintenance data, and expected life data;

[0040] Compare the remaining life data with multiple preset thresholds to determine the operating status of rail transit equipment.

[0041] Specifically, the expected life data may be the average of theoretical life data and simulated life data. The theoretical life data may be obtained through the factory nameplate of the rail transit equipment, and the simulated life data may be obtained by constructing a simulation model of the rail transit equipment, and designing simulation scenarios through the orthogonal experimental method under various environmental factors, various usage frequencies, various maintenance conditions, and equipment aging parameters. The simulation model of the rail transit equipment is simulated under the simulation scenario, and the corresponding simulated life data is obtained according to the simulation operation results, thereby improving the accuracy of the expected life data.

[0042] In a specific embodiment, the specific process of determining the operating status of the rail transit equipment includes:

[0043] When the remaining life data is less than or equal to a first preset threshold, the rail transit equipment is classified and the corresponding rail transit equipment is replaced according to the importance.

[0044] In a specific embodiment, the specific process of determining the operating status of the rail transit equipment further includes:

[0045] When the remaining life data is greater than a first preset threshold, a fault prediction is performed on the rail transit equipment to determine a corresponding fault prediction result.

[0046] In a specific embodiment, the specific process of determining the corresponding fault prediction result includes:

[0047] When the remaining life data is greater than a first preset threshold and less than or equal to a second preset threshold, constructing an equipment failure analysis model;

[0048] Input the real-time operation data, image data, operation and maintenance data, and remaining life data into the equipment failure analysis model to perform failure prediction and obtain the corresponding failure prediction results;

[0049] Carry out preventive maintenance on rail transit equipment based on fault prediction results.

[0050] Specifically, the fault analysis model can be a comprehensive network combining a convolutional neural network and a fuzzy neural network, which can better extract useful features from the original data, and at the same time use the useful features as the input of the fuzzy neural network to obtain more accurate fault prediction results.

[0051] In a specific embodiment, the specific process of determining the operating status of the rail transit equipment further includes:

[0052] When the remaining life data is greater than the second preset threshold, the real-time operation data and image data of the rail transit equipment continue to be monitored.

[0053] Specifically, when the remaining life data is greater than the second preset threshold and the duration of continuing to monitor the real-time operation data and image data of the rail transit equipment reaches the time threshold, the real-time operation data and image data are input into the fault analysis model for fault prediction, and the rail transit equipment is adjusted according to the fault prediction results at this time, so as to timely discover potential faults of the rail transit equipment and improve operating efficiency.

[0054] In a specific embodiment, the specific process of determining the remaining life data of the rail transit equipment includes:

[0055] The operation and maintenance data are divided to obtain the corresponding positive operation index data and negative operation index data. At the same time, the real-time operation data and image data are divided to determine the corresponding positive data and negative data;

[0056] Determine the weights of positive operating indicator data and negative operating indicator data, and the weights of positive data and negative data;

[0057] A regression model is constructed, and the positive operation indicator data, negative operation indicator data, positive data and negative data are normalized, and the remaining life data is obtained by combining the corresponding weights.

[0058] Specifically, in the operation data of rail transit equipment, positive data generally refers to the data of the normal state of the equipment and the data that is conducive to normal operation, and negative data generally refers to the data when the equipment is operating abnormally or beyond the expected range. These data usually indicate that there is a problem or failure in the equipment, which requires attention and corresponding measures. The weights of positive operation indicator data and negative operation indicator data, and the weights of positive data and negative data can be determined by the entropy weight method.

[0059] See also Figure 2 As shown, an embodiment of the present invention further provides a system for detecting the state of rail transit equipment using any one of the above embodiments, including:

[0060] A collection module is used to collect real-time operation data, image data, operation and maintenance data, and life expectancy data of rail transit equipment within a preset period of time;

[0061] A calculation module, used to determine the remaining life data of rail transit equipment based on real-time operation data, image data, operation and maintenance data, and expected life data;

[0062] The judgment module is used to compare the remaining life data with multiple preset thresholds to judge the operating status of the rail transit equipment.

[0063] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0064] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for detecting the state of rail transit equipment, characterized in that: The following steps are involved: Collect real-time operation data, image data, operation and maintenance data, and life expectancy data of rail transit equipment within a preset period; Determine the remaining life data of the rail transit equipment according to the real-time operation data, image data, operation and maintenance data, and expected life data; Compare the remaining life data with multiple preset thresholds to determine the operating status of rail transit equipment.

2. A method for detecting the state of rail transit equipment according to claim 1, characterized in that: The specific process of judging the operating status of rail transit equipment includes: When the remaining life data is less than or equal to a first preset threshold, the rail transit equipment is classified and the corresponding rail transit equipment is replaced according to the importance.

3. A method for detecting the state of rail transit equipment according to claim 2, characterized in that: The specific process of judging the operating status of rail transit equipment also includes: When the remaining life data is greater than a first preset threshold, a fault prediction is performed on the rail transit equipment to determine a corresponding fault prediction result.

4. A method for detecting the state of rail transit equipment according to claim 3, characterized in that: The specific process of determining the corresponding fault prediction result includes: When the remaining life data is greater than a first preset threshold and less than or equal to a second preset threshold, constructing an equipment failure analysis model; Input the real-time operation data, the image data, the operation and maintenance data, and the remaining life data into the equipment failure analysis model to perform failure prediction and obtain corresponding failure prediction results; Preventive maintenance is performed on the rail transit equipment according to the fault prediction result.

5. A method for detecting the state of rail transit equipment according to claim 4, characterized in that: The specific process of judging the operating status of rail transit equipment also includes: When the remaining life data is greater than a second preset threshold, the real-time operation data and image data of the rail transit equipment continue to be monitored.

6. A method for detecting the state of rail transit equipment according to claim 1, characterized in that: The specific process of determining the remaining life data of rail transit equipment includes: The operation and maintenance data are divided to obtain corresponding positive operation index data and negative operation index data, and the real-time operation data and the image data are divided to determine corresponding positive data and negative data; Determining the weights of the positive operation indicator data and the negative operation indicator data, and the weights of the positive data and the negative data; A regression model is constructed, and the positive operation index data and the negative operation index data, the positive data and the negative data are normalized, and the remaining life data is obtained by combining the corresponding weights.

7. A system using a state detection method for rail transit equipment according to any one of claims 1 to 6, characterized in that: include: A collection module is used to collect real-time operation data, image data, operation and maintenance data, and life expectancy data of rail transit equipment within a preset period of time; A calculation module, used to determine the remaining life data of the rail transit equipment according to the real-time operation data, image data, operation and maintenance data and expected life data; The judgment module is used to compare the remaining life data with multiple preset thresholds to judge the operating status of the rail transit equipment.

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