Fault detection and diagnosis of relative position detection sensor for high speed maglev train based on kernel principal component analysis

Chunhui Dai; Peng Deng; Zhiqiang Long

doi:10.1117/12.2559466

31 December 2019 Fault detection and diagnosis of relative position detection sensor for high speed maglev train based on kernel principal component analysis

Chunhui Dai, Peng Deng, Zhiqiang Long

Author Affiliations +

Proceedings Volume 11384, Eleventh International Conference on Signal Processing Systems; 113841C (2019) https://doi.org/10.1117/12.2559466
Event: Eleventh International Conference on Signal Processing Systems, 2019, Chengdu, China

Abstract

Relative position detection sensor of high speed maglev train is one of the most important sensors in train positioning and speed measurement system. There is a complex circuit structure inside the sensor. How to ensure the reliability of sensors is the key problem to ensure the safe operation of maglev train, it is necessary to detect and diagnose the faults of the sensor which has been replaced or just left the factory. Kernel principal component analysis (KPCA) is used to diagnose sensor faults in this paper. This method is based on sensor data. It has the advantages of simplicity, convenience and high accuracy. The simulation and experimental results show that this method has a good effect on sensor detection and diagnosis.

Citation Download Citation

Chunhui Dai, Peng Deng, and Zhiqiang Long "Fault detection and diagnosis of relative position detection sensor for high speed maglev train based on kernel principal component analysis", Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 113841C (31 December 2019); https://doi.org/10.1117/12.2559466

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