Minimization of the vestigial noise problem of empirical wavelet transform to detect bearing faults under time-varying speeds

This work proposes a systematic approach to detect and classify bearing faults using vibration signals under varying speeds. The proposed approach consists of several steps, such as segmentation of signal consisting of maximum fault relevant information and extraction of features less influenced by varying speeds, and develop a machine learning model for online classification of bearing faults. Bearing when operating under time-varying speeds, the most critical and challenging step, is the demodulation of non-stationary and nonlinear vibration signals exhibiting severe modulations. The empirical wavelet transformation (EWT) algorithm has been used to decompose the raw signal into multiple mode functions (MFs), thereby detecting faults. However, these MFs contaminated by vestigial noise, when processed, mislead the detection of incipient bearing faults, thereby reducing EWT performance. Hence, this study addresses this by proposing the selection of the most impulsive MF for varying speed by estimating instantaneous frequency, which lies near bearing characteristic defect frequencies, thereby eliminating the possibility of vestigial noise being processed. Further, ten entropies, root-mean-square, and kurtosis are computed from the selected MF for statistical analysis. The results of the proposed approach are compared with the ensemble empirical mode decomposition to highlight the capabilities. Statistically significant fault discriminating features are identified using the Kruskal–Wallis test. These identified features are subsequently utilized by the Random Forest classifier. Thus, it has resulted in higher accuracy in detecting and classifying the different faults trapped by severe modulations.