| Citation: | JIANG D N，BA Y J，LI W. Sensor fault detection and data reconstruction method of power supply vehicle[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（7）：1583-1592 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0501
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Aiming at the problem that the power supply vehicle is prone to sensor fault due to the complex operating environment, a sensor fault detection and data reconstruction method based on spatiotemporal correlation is proposed in this paper. Firstly, according to the time-series relationship characteristics of single sensor operation data, the fault detection sub-model of the power vehicle sensor is established with the help of the selective forgetting extreme learning machine (SF-ELM) mechanism, and the fault detection of power vehicle sensor is realized. Secondly, simultaneous interpreting the fault sensors, using the spatial correlation among different sensors, and through redundancy analysis, the improved mutual information entropy is used to screen out the auxiliary sensor data which is highly correlated with the fault sensor data, and the online reconstruction of the failure sensor data is realized. Finally, the feasibility and effectiveness of the proposed method in sensor fault detection and data reconstruction of power vehicles are verified by simulation.
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