An improved fusion prognostics method for remaining useful life prediction of bearings

The remaining useful life (RUL) prediction of bearings has emerged as a critical technique for providing failure warnings in advance, reducing costly unscheduled maintenance and enhancing the reliability of bearings. Recently, a fusion prognostics method combining exponential model and relevance vector machine (RVM) has been proposed and applied to the RUL prediction of bearings. This fusion prognostics method integrates the advantages of RVM and exponential model and so has better prediction performance than other exponential model-based methods. However, selecting the appropriate value of kernel parameter is very difficult for this fusion prognostics method because of the lack of an explicit prior knowledge. which reduces the prediction accuracy of the fusion prognostics method and affects its generalization performance. To solve this problem, an improved fusion prognostics method is proposed in this paper. In the improved fusion prognostics method, RVM regressions with different kernel parameter values are first applied to obtaining different sparse datasets. Then, using the exponential model of bearing degradation, the different degradation curves are got by fitting the obtained sparse datasets and the Fréchet distance is employed to select the optimum degradation curve from those fitted curves. Finally, the RUL is predicted by extrapolating the selected degradation curve to reach the failure threshold. To verify the superiority of the proposed method compared with the original fusion prognostics method, a real bearing degradation data is used for the RUL prediction. The results show that the improved fusion prognostics method outperforms the original fusion prognostics method in the RUL prediction of bearings.