Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM

Abstract

Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the degradation information, the bearing performance degradation assessment based on SC-RMI and Student’s t-HMM is proposed in this article. Firstly, spectral clustering was used as a preprocessing step to cluster features with similar degradation curves. Then, rank mutual information, which is more suitable for trendability estimation of long time series, was utilized to select the optimal feature from each cluster. The feature selection method based on these two steps is called SC-RMI for short. With the selected features, Student’s t-HMM, which is more robust to outliers, was utilized for performance degradation modeling and assessment. The verifications based on an accelerated life test and the public XJTU-SY dataset showed the superiority of the proposed method.

Mechanical Equipment Performance Degradation Assessment Method Based on Improved Degradation Feature Extraction and Sample Completeness

“…Chen et al 7 proposed a degradation index called SC-RMI based on Student's t-HMM model to evaluate the degradation state of bearings. Hong et al 8 extracted the degradation feature to assess the performance of power transformer windings based on the fusion method of VMD and WT.…”