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Time-series Independent Component Analysis-aided Fault Detection for Running Gear Systems

Chao Cheng*, Sheng Yang, Yu Song, and Gang Liu
International Journal of Control, Automation, and Systems, vol. 20, no. 9, pp.2892-2901, 2022

Abstract : By dealing with the non-Gaussian measurement and slow-change faults in running gear systems, this paper presents a fault detection (FD) scheme named time-series independent component analysis (TsICA), where the time-series characteristic is taken into account. Time-series algorithms can extract slow-change information in the data. The advantages of the proposed method are: 1) it can improve the FD power; 2) it considers the information in the data 3) it is suitable for non-Gaussian systems; 4) it is sensitive to slow-change faults; 5) it can effectively shorten the first time of fault detection. The feasibility of the proposed scheme is verified through a case study on running gear systems.

Keyword : Fault detection (FD), non-Gaussian, running gear systems, time-series independent component analysis (TsICA).

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