A deep learning based health indicator construction and fault prognosis with uncertainty quantification for rolling bearings

In this paper, a hybrid convolutional neural network (CNN)-bidirectional gated recurrent unit (BiGRU) model is integrated with the bootstrap method to endow the deep learning (DL) based prognostic method with the quantification capability of the prognostic intervals. The proposed hybrid method contains three parts: (I) The complete ensemble empirical mode decomposition with adaptive noise and principal component analysis and the CNN-BiGRU are utilized to automatically construct the health indicator (HI). (II) 3 σ criterion is employed to detect the first predicting time based on the HIs of rolling bearings. (III) The bootstrap method is imposed to endow the proposed DL method with the quantification capability of the prognostic intervals. The experimental validation is carried out on the XJTU-SY bearing dataset and the proposed method outperforms the other four methods in the majority of cases. In addition, the proposed method not only comprehensively considers the fault prognosis error caused by model parameters and noise, but also considers the prediction error caused by different combinations of features on the model.