WPT-Base Selection for Bearing Fault Feature Extraction: A Node-Specific Approach Study

Abstract

Wavelet packet transform (WPT) has found extensive use in bearing fault diagnosis for its ability to provide more accurate frequency and time-frequency representations of non-stationary signals. Traditional quantitative methods prioritize unequal node-energy distribution at the desired decomposition level as a criterion for WPT base selection. Decomposition results obtained with WPT-base selected using this approach can be characterized as having one WPT-node with high signal energy which is automatically considered as a component of interest containing information about bearing fault. However, prioritizing one WPT-node at this early stage of fault diagnosis process might not be optimal for all nodes in the WPT-tree decomposition level and might exclude components in other nodes, which may contain features potentially important for fault diagnosis. In this paper, we propose a node-specific approach for WPT-base selection to improve the quality of feature extraction. The new criterion evaluates WPT-bases upon their ability to generate a signal with the highest ratio of energy and entropy of the signal spectrum for a specific node. Using this criterion, the final WPT signal decomposition is constructed using the WPT-nodes produced by the bases with the highest criterion score. This approach ensures the preservation of all meaningful components in each node and their distinction from the noisy background, resulting in a higher quality feature extraction. To evaluate the effectiveness of the proposed method for bearing fault diagnosis, a comparative analysis was conducted using two sets of Paderborn University bearing fault experimental vibration data and the bearing vibration data from the Case Western University benchmark dataset. As a result, the proposed method showed better average performance across three datasets.