An Integrated Method for Bearing State Change Identification and Prognostics Based on Improved Relevance Vector Machine and Degradation Model

Bearings are classified as one of the most safety-critical components in industrial machinery, and their prognostics have proven to be an efficient way to reduce costly unplanned maintenance and guarantee reliability and safety. However, accurate data-driven remaining useful life (RUL) prediction remains a significant challenge because of nonlinear degradation and prediction uncertainty. In this study, an integrated prognostics method is designed for a rolling element bearing to identify its health state changes adaptively and further improve the accuracy of RUL prediction, particularly for its long-term prediction. First, a new health indicator (HI) is proposed based on fuzzy c-means clustering and a modified confidence value (CV). The former makes the HI sensitive to state changes, and the latter improves its monotonicity and smoothness. A relevance vector machine (RVM) regression model functions with multikernel widths with a modified degradation model to improve the accuracy of RUL prediction, even when fewer samples are available for long-term prediction. The experimental results of the two case studies indicate that the proposed HI provides a better representation for bearing monotonic and smooth degradation processes while automatically identifying its first predicting time and failure threshold. Moreover, the proposed RUL prediction method exhibits stable performance for various prediction ranges and higher accuracy for bearings with different degradation rates.

Automated summary

This study proposes an integrated prognostics method for rolling element bearings to improve the accuracy of remaining useful life (RUL) prediction. A new health indicator is introduced and a relevance vector machine regression model is used to achieve higher accuracy even for long-term predictions.