Composite fault feature extraction of rolling bearing using adaptive circulant singular spectrum analysis

Abstract

Aiming to extract the weak composite fault characteristics of a rolling bearing under harsh operation conditions, a novel composite fault diagnosis method for bearings based on adaptive circulant singular spectrum analysis (ACiSSA) is proposed. The proposed method is able to adaptively obtain the eigenvalue of a non-stationary vibration signal in any dimension, and effectively reassemble the same frequency components and improve the signal-to-noise ratio (SNR). Specifically, circulant singular spectrum analysis is utilized to decompose the raw signal, and the optimal parameters, i.e. the embedding dimension and threshold value of cumulative contribution, are selected to maximum kurtosis through the grey wolf optimization method. The signal is reconstructed with high SNR according to the effective singular spectrum components. Envelope demodulation analysis is then implemented to extract the characteristic defect frequency in the reconstructed signal. Finally, feature extraction performance is quantitatively evaluated, and experimental results show that the proposed ACiSSA method is able to extract more sensitive features under more noisy conditions compared with other common methods, with higher computational efficiency.