Adaptive singular value decomposition for bearing fault diagnosis under strong noise interference

Abstract

Abstract Singular value decomposition (SVD) is an effective tool for analyzing the signals from mechanical systems and for fault diagnosis, which is a non-parametric signal analysis method free from phase shift and waveform distortion. In SVD, the embedding dimension of the Hankel matrix is an important parameter that directly influences the effectiveness of the SVD. However, the embedding dimension is usually determined by experience, which is quite subjective and limits the applicability of SVD. As such, a novel SVD method, named adaptive SVD (ASVD), is proposed in this paper. In ASVD, novel criteria are defined to obtain the specific embedding dimensions for different mechanical signals by means of numerical simulation. A novel phenomenon, that the singular value pairs change periodically with the step size of half-cycle sampling points, is found and it can be used to calculate specific embedding dimension instead of selecting it from a range using experience. Meanwhile, the envelope spectral amplitude ratio index is developed for addressing the issue of excessive decomposition in classic SVD. Lastly, an ASVD-based bearing fault diagnosis method is proposed to adaptively select useful sub-signals and to detect faults. Both simulated signal and experiment signals, collected from different bearing test rigs are used to verify the effectiveness of the proposed method. The results show that the proposed method has a satisfactory ability to eliminate interference noise and detect bearing fault.