A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification. (20th January 2015)

• Record Type: Journal Article
• Authors: Ding, Xiaoxi; He, Qingbo; Luo, Nianwu
• Is Part Of: Journal of sound and vibration. Volume 335(2015)
• Page Start: 367
• Page End: 383
• Publication Date: 2015-01-20
• Subjects: Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2014.09.026
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: 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. Highlights: A fusion feature based on LPP is extracted for bearing fault classification. The new feature highlights the fault dissimilarity based on the healthy pattern. The new captured feature has a much better clustering property. The effectiveness is verified in bearing fault diagnosis under different loads. An improved fusion feature based on LGPCA is verified to have a better performance.