Fault detection for rotating machinery using translation-invariant higher-density wavelet packet sliding window block thresholding

Abstract

The weak incipient fault feature information of mechanical equipment is often submerged in strong background noise, which increases the difficulty of feature extraction. Herein, a novel method called the translation-invariant higher-density wavelet packet sliding window block thresholding is proposed. A higher-density wavelet transform processes one scale function and two wavelet functions, which has an advantage over single wavelet transform in matching various signal feature information. The translation-invariant higher-density wavelet packet transform effectively eliminates the Gibbs phenomenon, and the fine division of frequency bands breaks through the limitations of the higher-density wavelet transform to obtain more comprehensive feature information. At the same time, to accommodate the periodicity of the impact feature signal and the correlation between the decomposition coefficients, a sliding window block thresholding method is proposed. The sliding window is set to independently retain the weak fault features and block thresholding is applied to select the optimal block length and threshold by minimizing the Stein unbiased risk estimator. The results of the simulation and experiment indicate that the proposed method can more effectively reduce the interference of background noise and impurity frequencies, and successfully extract the characteristic frequencies of incipient fault diagnosis.