A state detection method and system for rail transit equipment

Abstract

The present invention provides a state detection method and system for rail transit equipment, which relate to the technical field of transportation equipment, wherein the method comprises the following steps: collecting real-time operation data, image data, operation and maintenance data and expected life data of rail transit equipment within a preset time period; determining the remaining life data of the rail transit equipment based on the real-time operation data, image data, operation and maintenance data and expected life data; comparing the remaining life data with multiple preset thresholds to judge the operating state of the rail transit equipment; the present invention can perform fault prediction based on relevant data of the rail transit equipment, and can make more targeted equipment maintenance preparations in advance. Compared with maintenance after equipment failure, it can shorten downtime, improve operational efficiency and reduce waste of maintenance resources.

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] Rail transit equipment is an integral part of the rail transit system. It is crucial for ensuring safe operation, improving the passenger experience, and guaranteeing system reliability. By continuously monitoring and analyzing the operating status of rail transit equipment, rail transit operators can promptly identify potential problems and take preventative measures, ensuring 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 cases where some components do not need to be repaired but are also inspected, or many components have failed before the maintenance time. On the one hand, this increases the probability of equipment failure, and on the other hand, it also wastes resources.

[0004] Therefore, how to provide a state detection method for rail transit equipment that can solve the above problems is an issue that those skilled in the art 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 rail transit equipment, and make more targeted equipment maintenance preparations in advance. Compared with repairing equipment after failure, it can shorten downtime, improve operational efficiency, and reduce the waste of maintenance resources.

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

[0007] A method for detecting the status 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] Determining remaining life data of rail transit equipment based on 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 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] Inputting 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 indicator data and the negative operation indicator data, the positive data and the negative data are normalized, and the remaining life data is obtained by combining corresponding weights.

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

[0026] The acquisition 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;

[0027] a calculation module, configured to determine the remaining life data of the rail transit equipment based on 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 provides a method and system for detecting the status of rail transit equipment, which has the following beneficial effects:

[0030] (1) The present invention can timely detect potential equipment failures and handle them in advance by real-time monitoring and analysis of relevant data on 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0034] Figure 1 This is an overall flow chart of a method for detecting the status of rail transit equipment provided by the present invention;

[0035] Figure 2 This is 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 clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts 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 method for detecting the status of rail transit equipment, comprising the following steps:

[0038] Collecting real-time operating data, image data, operation and maintenance data, and life expectancy data of rail transit equipment within a preset time period. The real-time operating data of rail transit equipment may include electrical data, vibration data, environmental data, energy consumption data, speed data, and communication status data. 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 signaling 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 can be the average of the theoretical life data and the simulated life data. The theoretical life data can be obtained through the factory nameplate of the rail transit equipment, and the simulated life data can be obtained by constructing a simulation model of the rail transit equipment, and designing simulation scenarios under various environmental factors, various usage frequencies, various maintenance conditions and equipment aging parameters through orthogonal experimental methods. 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 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 that combines a convolutional neural network and a fuzzy neural network, which can better extract useful features from the original data and use the useful features as input to 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 a 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 rail transit equipment includes:

[0055] Divide the operation and maintenance data to obtain the corresponding positive operation indicator data and negative operation indicator data. At the same time, divide the real-time operation data and image data to determine the corresponding positive data and negative data.

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

[0057] A regression model is constructed, and the positive operation indicator data and 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, within rail transit equipment operating data, positive data generally refers to data indicating the equipment is in normal operation and is conducive to normal operation. Negative data generally refers to data when the equipment is operating abnormally or outside of expected ranges. This data typically indicates a problem or malfunction with the equipment, requiring attention and appropriate action. The weights of positive and negative operating indicator data, as well as the weights of positive and negative data, can be determined using 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, comprising:

[0060] The acquisition 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;

[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] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0064] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one 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 is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for detecting the status 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; Determining remaining life data of rail transit equipment based on 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. The specific process includes: When the remaining life data is less than or equal to a first preset threshold, classifying the rail transit equipment and replacing the corresponding rail transit equipment according to their importance; When the remaining life data is greater than a first preset threshold, performing fault prediction on the rail transit equipment and determining a corresponding fault prediction result; 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; Inputting 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; Performing preventive maintenance on the rail transit equipment according to the fault prediction result; When the remaining life data is greater than the second preset threshold, continue to monitor the real-time operation data and image data of the rail transit equipment; 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 a time threshold, input the real-time operation data and image data into the fault analysis model for fault prediction.

2. 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 indicator data and the negative operation indicator data, the positive data and the negative data are normalized, and the remaining life data is obtained by combining corresponding weights.

3. A system using the state detection method of a rail transit equipment according to any one of claims 1-2, characterized in that: include: The acquisition 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; a calculation module, configured to determine the remaining life data of the rail transit equipment based on 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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