A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification

The sensitive feature extraction from vibration signals is still a great challenge for effective fault classification of rolling element bearing. Current fault classification generally depends on feature pattern difference of different fault classes. This paper explores the active role of healthy pattern in fault classification and proposes a new fusion feature extraction method based on locality preserving projections (LPP). This study intends to discover the local feature pattern difference between each bearing status and the healthy condition to characterize and discriminate different bearing statuses. Specifically, the proposed fusion feature is achieved by two main steps. In the first step, a two-class model is firstly constructed for each class by using this class of signals and healthy condition signals. Then a fusion mapping is generated by mathematically combing the mappings of the LPP or its improvement for all two-class models. In the second step, the LPP is further applied to reduce the fusion mapping dimension, which is to find more sensitive low-dimensional information hidden in the high-dimensional fusion feature structure. The final achieved fusion feature can enhance the discrimination between all classes by improving the between-class scatter and within-class scatter for fault classification. Experimental results using different bearing fault types and severities under different loads show that the proposed method is well-suited and effective for bearing fault classification.