The prediction of remaining useful life (RUL) of mechanical equipment provides a timely understanding of the equipment degradation and is critical for predictive maintenance of the equipment. In recent years, the applications of deep learning (DL) methods to predict equipment RUL have attracted much attention. There are two major challenges when applying the DL methods for RUL prediction: (1) It is difficult to select the prediction model structure and hyperparameters such as network depth, learning rate, batch size, and etc. (2) The developed prediction model is domain dependent, i.e., it can only give good prediction performance in one data domain (one particular type of working conditions and fault modes). In order to meet the challenges, a novel RUL prediction method developed using a deep convolutional neural network (DCNN) combined with Bayesian optimization and adaptive batch normalization (AdaBN) is presented in this paper. The proposed RUL prediction model is validated by the turbofan engine degradation simulation dataset provided by NASA. The prediction results show that the proposed prediction model provides better prediction results than model structures obtained by random search and grid search. The results also show that the domain adaptation capability of the prediction model has been improved. INDEX TERMS Remaining useful life prediction, Bayesian optimization, adaptive batch normalization, domain adaptation.

he remaining useful life (RUL) prediction methods based on deep neural networks (DNN) have received much attention in recent years. The collected time-series signals are usually processed by the sliding time window method into several segments with the same sequence length as input. However, the signals processing is not only time-consuming but also relies too much on personal experience. Moreover, the length of the time window affects the prediction results and the prediction range. Obviously, it is more desirable to remove the data processing and use an entire time series signal as input to predicting remaining useful life, i.e., sequence-to-sequence RUL prediction. In order to remove the shortcomings of signal processing, this paper uses long short-term memory (LSTM) and encoding-decoding framework to construct an unsupervised sequence data processing model. Then, a temporal convolutional network (TCN) based on the convolutional neural network (CNN) is used to further process the output data of the unsupervised sequence data processing model. The proposed sequence-to-sequence remaining useful life prediction method can not only maintains the complete sequence of the data but has a good capability of the data processing. The open access C-MAPSS simulation datasets is used for validation. The validation results show that the proposed method can realize unsupervised sequence signal reconstruction. Moreover, it has a better prediction results and prediction efficiency.

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