Fault prediction method and system for rail braking equipment

By constructing a dynamic fault association graph and a multi-task predictive maintenance decision tree, the problems of resource waste and safety hazards in the maintenance of track braking equipment are solved, and accurate fault prediction and scientific maintenance decisions are achieved, thereby improving equipment reliability and safety.

CN120804570AActive Publication Date: 2025-10-17BEIJING MAGLEV DATA TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510868964.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-26
Publication Date
2025-10-17
Estimated Expiration
2045-06-26

AI Technical Summary

Technical Problem

The existing maintenance model for track braking equipment mainly relies on periodic inspections and post-incident repairs, which cannot adapt to individual differences in equipment, leading to resource waste or safety hazards. Furthermore, it lacks accurate prediction of fault development trends and support for maintenance decision-making.

Method used

A dynamic fault association graph is constructed, and directed edges are built through physical connections, energy transfer, and control signal flow. The edge weights are dynamically adjusted in combination with real-time data. The PageRank value is used to identify key components, and the Dijkstra algorithm is used to find fault propagation paths. A multi-task predictive maintenance decision tree is constructed, and multi-dimensional prediction results are output.

Benefits of technology

It enables comprehensive and three-dimensional insight into track braking equipment failures, accurately identifies key components and failure propagation paths, scientifically allocates maintenance resources, avoids resource waste and operational interruptions, and improves equipment reliability and safety.

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Abstract

The invention relates to the technical field of traffic fault prediction, in particular to a rail braking equipment fault prediction method and system, and the method comprises the steps: obtaining the operation state data of rail braking equipment; a dynamic fault association diagram is constructed, and a fault transmission relation between brake equipment components is obtained; dynamically adjusting the edge weight of the dynamic fault association diagram according to the running state data of the track braking equipment, calculating the PageRank value of the node of the dynamic fault association diagram to identify a key component, and searching a target fault propagation path by adopting a Dijkstra algorithm; constructing a multi-task predictive maintenance decision-making tree, inputting the running state data of the track braking equipment, the key component and the target fault propagation path into the multi-task predictive maintenance decision-making tree, and outputting a multi-dimensional prediction result through multi-layer decision-making; and generating a priority maintenance suggestion according to the multi-dimensional prediction result. Therefore, the problems that dynamic changes of the components are ignored, the relation between the components is cut, and maintenance decision support is lacked in the prior art are solved.
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