Machine Fault Classification Based on Local Discriminant Bases and Locality Preserving Projections

Machine fault classification is an important task for intelligent identification of the health patterns for a mechanical system being monitored. Effective feature extraction of vibration data is very critical to reliable classification of machine faults with different types and severities. In this paper, a new method is proposed to acquire the sensitive features through a combination of local discriminant bases (LDB) and locality preserving projections (LPP). In the method, the LDB is employed to select the optimal wavelet packet (WP) nodes that exhibit high discrimination from a redundant WP library of wavelet packet transform (WPT). Considering that the obtained discriminatory features on these selected nodes characterize the class pattern in different sensitivity, the LPP is then applied to address mining inherent class pattern feature embedded in the raw features. The proposed feature extraction method combines the merits of LDB and LPP and extracts the inherent pattern structure embedded in the discriminatory feature values of samples in different classes. Therefore, the proposed feature not only considers the discriminatory features themselves but also considers the dynamic sensitive class pattern structure. The effectiveness of the proposed feature is verified by case studies on vibration data-based classification of bearing fault types and severities.