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Performance degradation assessment has been proposed to realize equipment's near-zero downtime and maximum productivity. Exploring effective indices is crucial for it. In this study, rolling element bearing has been taken as a research object, spectral entropy is proposed to be as a complementary index for its performance degradation assessment, and its accelerated life test has been performed to collect vibration data over a whole lifetime (normal-fault-failure). Results of both simulation and experiment show that spectral entropy is an effective complementary index.

Bearing performance degradation assessment is more effective than fault diagnosis to realize condition-based maintenance. In this article, a hybrid model is proposed for it based on a support vector data description (SVDD) and fuzzy c-means (FCM). SVDD, which holds excellent robustness to outliers, is used to obtain the clustering centre of normal state. The subjection of tested data to normal state is defined as a degradation indicator, which is computed by a FCM algorithm with final failure data. The results of applying this hybrid model to an accelerated bearing life test show that it can effectively assess bearing performance degradation. Furthermore, it is robust to the outliers in the training set and is not influenced by the Gaussian kernel parameter.

Exploring an effective assessment index is significant for performance degradation assessment, which has been proposed to realize equipments’ near-zero downtime and maximum productivity. In this paper, the Kolmogorov-Smirnov test based on an autoregressive model is proposed to assess the performance degradation of rolling bearings. Accelerated life test (in Hangzhou Bearing Test and Research Center) of rolling bearings was performed to collect vibration data over a whole lifetime (normal-fault-failure). The result shows that the Kolmogorov-Smirnov test method can obviously detect incipient weak defects and can reflect performance degradation process well. In particular, it can detect abnormal stages earlier before the bearing steps into failure, which is significant in condition maintenance and prognosis.