A novel method of combining nonlinear frequency spectrum and deep learning for complex system fault diagnosis. (February 2020)

• Record Type: Journal Article
• Authors: Chen, Lerui; Zhang, Zerui; Cao, Jianfu; Wang, Xiaoqi
• Is Part Of: Measurement. Volume 151(2020)
• Publication Date: 2020-02
• Subjects: Nonlinear frequency spectrum -- Output frequency response functions -- Stack denoising auto-encoders -- Fault diagnosis; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.107190
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The calculation model of NOFRF is simple, which can accelerate the calculation speed. SDAE extracts fault features layer by layer to improve the feature extraction ability. Denoising module in SDAE can reduce the interference of noise to diagnosis accuracy. Abstract: A novel fault diagnosis method is proposed for complex systems by combining nonlinear frequency spectrum and stacked denoising auto-encoders (SDAE). In order to solve the problem of large calculation amount of generalized frequency response functions (GFRF), one-dimensional nonlinear output frequency response functions (NOFRF) are used to obtain nonlinear frequency spectrum. In order to overcome the problem of weak ability of fault features extraction, stacked denoising auto-encoders (SDAE) neural network is adopted to extract the fault features from nonlinear frequency spectrum. In this novel method, four orders nonlinear frequency spectrum of each state of Permanent Magnet Synchronous Motor (PMSM) are obtained by identification algorithm; Then, choosing suitable sampling points from four orders frequency spectrum to construct high-dimensional data; Finally, stacked denoising auto-encoders (SDAE) neural network is designed to realize the output of fault classification. Simulations indicate that the proposed method has good real-time performance and high diagnosis accuracy.