| Citation: | ZHU P R，LIU Y Z，LIU Z C，et al. Fault diagnosis of synchronous generator rotating rectifier based on CEEMD and improved ELM[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（5）：1166-1175 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0376
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Aiming at the problems of slow diagnostic speed and difficulty in pardmeter selection in artifical intelligence algorithms currently used in fault diagnosis of aviation generator rotating rectifiers, an extreme learning machine (SSA-ELM) and complementary ensemble empirical mode decomposition (CEEMD) based fault diagnosis approach is investigated. In the finite element software Maxwell and Simplorer, the three-stage motor model is built, the excitation current signal is collected, the excitation current signal is decomposed into a series of modal components by CEEMD, and the fault characteristic parameters are constructed. Then the training parameters and parameters of the extreme learning machine are optimized by the SSA algorithm, and the fault is diagnosed. Finally, it is verified by the experimental platform. The results show the effectiveness of the three-stage synchronous motor finite element model.Compared with the existing methods, the rotating rectifier diode fault diagnosis method based on CEEMD and SSA-ELM has higher fault diagnosis accuracy and classification speed.
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