A BiGRU method for remaining useful life prediction of machinery

Abstract

Remaining useful life (RUL) prediction, allowing for mechanical prediction maintenance, reduces the unplanned expensive maintenance greatly. Deep learning methods have provided better point estimation for RUL prediction due to their powerful feature extraction capability. Because of the measurement noise and model parameters, the prediction results usually vary greatly. In order to express the uncertainty of prediction, it is necessary to calculate not only the determined RUL prediction value, but also the confidence interval (CI) of RUL. In this paper, a bidirectional gated recurrent unit (BiGRU) RUL prediction method based on bootstrap method is proposed. The confidence interval (CI) of RUL can be obtained through bootstrap method. The validity of the proposed method is demonstrated by ABLT-1A bearing data. Obtaining the uncertainty in the RUL prediction has great significance for the actual production and manufacturing.