A new bearing weak fault diagnosis method based on improved singular spectrum decomposition and frequency-weighted energy slice bispectrum. (15th December 2020)

• Record Type: Journal Article
• Authors: Mao, Yongjie; Jia, Minping; Yan, Xiaoan
• Is Part Of: Measurement. Volume 166(2020)
• Publication Date: 2020-12-15
• Subjects: Singular spectrum decomposition -- Embedding dimension -- Frequency-weighted energy operator -- Slice bispectrum -- Rolling element bearing; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108235
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An improved SSD based on pre-selection for the embedding dimension is proposed to suppress mode mixing. FWESB is presented for removing uncoupled frequency component and enhance fault characteristics. A novel fault diagnosis scheme combining ISSD and FWESB is formulated. Simulated, experimental and engineering examples highlight the superiority of the proposed method. Abstract: The embedding dimension parameter has a significant effect on the decomposition results of singular spectrum decomposition (SSD). However, in the conventional SSD method, the embedding dimension of each iteration is determined by the empirical formula, which may lead to the problem of mode mixing and over decomposition. Aiming at this issue, mode mixing index (MMI) and over decomposition index (ODI) are proposed in this paper, and the product of the two indices is used as the basis for selecting the optimal embedding dimension from preset interval for each iteration. Subsequently, to extract the weak fault symptoms from the susceptive mode components, a new spectrum analysis technique named frequency-weighted energy slice bispectrum (FWESB) is proposed. In this method, Gaussian white noise and uncoupled frequency component in the signal are suppressed, thereby highlighting the weak shock fault characteristics in the vibration signal. The analysis results of the simulation and the experiment prove the validity of the proposed method in alleviating mode mixing and extracting weak fault symptoms of rolling bearings. Besides, the average results of multiple tests in experimental and engineering applications show that for experimental signals, compared with SSD, empirical wavelet transform (EWT) and fast kurtogram (FK), the characteristic frequency intensity coefficient (CFIC) of the proposed method are increased by 210.36%, 46.30% and 187.86% respectively. For practical engineering signals, compared with SSD, EWT and FK, the CFIC of the proposed method are increased by 127.13%, 190.89% and 301.51%, respectively.