Cross-fuzzy entropy-based approach for performance degradation assessment of rolling element bearings

Exploring effective indicators is significant for the assessment of the bearing performance degradation, which is crucial to realize the condition-based maintenance. In this paper, the cross-fuzzy entropy is introduced and is used to measure the similarity of patterns between normal signals and tested signals of the rolling element bearings, and the degree of similarity is used as an indicator of the bearing performance degradation. The original cross-fuzzy entropy focuses on the local characteristics of the signal and neglects its global trend. However, the global characteristics and global trends of bearing vibration signals may vary as the bearings degrade gradually. Therefore, a change has been made in the implementation of the original cross-fuzzy entropy algorithm to overcome this limitation and the modified cross-fuzzy entropy is more suitable for reflecting the whole degradation process of rolling element bearings. The experimental results demonstrate that the modified cross-fuzzy entropy can assess the bearing performance degradation process over their whole life time clearly and effectively.