Abstract
The problems in the remaining useful life (RUL) prediction of rolling bearings are difficulty in determining the failure threshold and large prediction error with a single prediction model. To solve them, based on the adaptive boosting integrated relevance vector machine model (AdaBoost_RVM), a method for constructing health indices and predicting RUL is proposed. The experimental results show that the failure thresholds of all the different bearings are 1 using the proposed method, and compared with the single RVM model, it yields a bearing RUL prediction that has a smaller error and is closer to the true value.
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