Journal
IEEE TRANSACTIONS ON POWER ELECTRONICS
Volume 36, Issue 7, Pages 7461-7469Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPEL.2020.3043741
Keywords
Learning systems; Fault diagnosis; Fault detection; Computer architecture; Sensor systems; Security; Principal component analysis; Broad learning system (BLS); fault detection and diagnosis (FDD); high-speed trains; principal component analysis (PCA); traction systems
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Funding
- National Natural Science Foundation of China [61903047]
- Jilin Science and Technology Department [20200401127GX]
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This article focuses on fault detection and diagnosis (FDD) for traction systems in high-speed trains, proposing an enhanced FDD architecture based on data-driven design which achieves fast and accurate fault detection without the need for mathematical models or control mechanisms of high-speed trains.
Faults happen inevitably in traction systems and thus place the security of the whole high-speed train at risk. In order to improve the safety and reliability of high-speed trains, this article deals with fault detection and diagnosis (FDD) problem for traction systems. Because of high sampling frequency of equipped sensors, FDD strategies in the supervision system of high-speed trains should be of enough high computation efficiency, which is a great bottleneck for artificial intelligence-based FDD methods. For reducing the computational load while maintaining the satisfactory diagnostic accuracy, an enhanced FDD architecture using the modified principal component analysis and broad learning system is developed in this article. Based on the proposed data-driven design whose core is to extract fault information, fast and accurate FDD can be achieved without requirements for mathematical models or control mechanism of high-speed trains. The effectiveness and feasibility of the proposed online design are illustrated on the traction control platform of high-speed trains.
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