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所属单位：自动化学院

发表刊物：Zhongguo Dianji Gongcheng Xuebao

摘要：This paper proposed a fault feature extraction method based on the stacked auto-encoder (SAE), which is optimized by the grey relational analysis (GRA). This method can extract fault features from raw data adaptively, and this method can be applied to fault diagnosis of rotating rectifier diodes in aerospace generator. First, filed current of aerospace generator excitation is collected. Second, the deep learning theory, combined with the grey relational analysis, is adopted to train the auto-encoder for achieving a good network structure of stack auto-encoders, which can extract the fault features adaptively from the generator current data information. Finally, fault diagnosis can be implemented with the support vector machine classifier. The performances of the presented method were compared with fast Fourier transform (FFT) method through simulations and physical experiments. The experiment results showed that the presented fault extractor is automatic and adaptive, and the achieved features with this method can be evaluated ideally with the support vector machine classifier. © 2017 Chin. Soc. for Elec. Eng.

ISSN号：0258-8013

是否译文：否

发表时间：2017-10-05

合写作者：Tang, Junxiang,龚春英,张卓然

通讯作者：崔江