Early identification of failures in rolling element bearings is an important research issue in mechanical systems. In this study, a reliable methodology for bearing fault detection is proposed, which is based on an optimal sub-band selection scheme using the discrete wavelet packet transform (DWPT) and envelope power analysis techniques. A DWPT-based decomposition is first performed to extract the characteristic defect features from the acquired acoustic emission (AE) signals. The envelope power spectrum (EPS) of each sub-band signal is then calculated to detect the characteristic defect frequencies to reveal abnormal symptoms in bearings. The selection of an appropriate sub-band is essential for effective fault diagnosis, as it can reveal intrinsically explicit information about different types of bearing faults. To address this issue, we propose a Gaussian distribution model-based health-related index (HI) that is a powerful quantitative parameter to accurately estimate the severity of bearing defects. The most optimal sub-band for fault detection is determined using two dimensional (2D) visualization analysis. The efficiency of the proposed approach is validated using several experiments in which different defect conditions are identified under variable, and low operational speeds.
Keywords: DWPT; bearing defects; envelope power spectrum (EPS); fault detection; health-related index (HI); sub-band analysis.