A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions

Remaining useful life (RUL) estimation plays a pivotal role in ensuring the safety of a machine, which can further reduce the cost by unwanted downtime or failures. A variety of data-driven methods based on artificial intelligence have been proposed to predict RUL of key component such as bearing. However, many existing approaches have the following two shortcomings: 1) the fault occurrence time (FOT) is ignored or selected subjectively; 2) the training and testing data follow the same data distribution. Inappropriate FOT will either include unrelated information such as noise or reduce critical degradation information. The prognostic model trained with dataset in one working condition can not generalize well on dataset from another different working condition owing to distribution discrepancy. In this paper, to handle these two shortcomings, hidden Markov model (HMM) is first employed to automatically detect state change so that FOT can be located. Then a novel transfer learning method based on multiple layer perceptron (MLP) is presented to solve distribution discrepancy problem. Experiment study on RUL estimation of bearing is analyzed to illustrate the effectiveness of the proposed method. The results demonstrate that the proposed framework can detect FOT adaptively, at the same time provide reliable transferable prognostics performance under different working conditions.