Bearing performance degradation assessment based on information-theoretic metric learning and fuzzy c-means clustering

Abstract

Bearing performance degradation assessment (PDA) is used to construct a degradation index (DI) to track the degradation process effectively and it plays an important role in the intelligent maintenance of rotating mechanical equipment. In order to construct a high quality DI of rolling bearing PDA processes, a bearing PDA model based on information-theoretic metric learning (ITML) and fuzzy C-means (FCM) clustering is proposed, which is called ITML-FCM in this paper. Firstly, the acquired vibration acceleration signals are decomposed into a set of intrinsic mode functions (IMFs) by variational mode decomposition (VMD) and fault features, such as singular value and relative energy of each IMF, are extracted to construct a high-dimensional feature space. Then, since the traditional distance metrics (i.e. Euclidean distance and Mahalanobis distance) ignore the label information of the samples, a distance metric matrix is learned using the ITML algorithm in the feature space based on the pairwise constraint from labeled training samples with normal and failure states. After that, the cluster centers of both the normal and the failure states are obtained using the FCM clustering algorithm, which is trained with the learned distance metric matrix. Finally, the DI is constructed using test samples' membership of a normal state. To evaluate the DI quality comprehensively, a weighted criterion using four metrics (i.e. trendability, monotonicty, robustness and discreteness) is proposed. Results of the analysis of the rolling bearing experimental data show that the distance metric matrix learned by ITML is conuctive to the classification of bearing degradation severity. Furthermore, the DI constructed by the ITML-FCM model shows superior comprehensive quality and, on average, the weighted criterion value is 8.45% higher than the FCM model using Euclidean distance and 19.71% higher than the FCM model using Mahalanobis distance.