A Semisupervised Deep Hybrid Multitask Model for RUL Prediction

Deep learning (DL) methods can be used to construct health indicators (HIs) for remaining useful life (RUL) prediction. Existing DL methods consider previous and current sensor signals and utilize labeled data, which are limited in practice. To leverage unlabeled data for extracting HIs, semi-supervised methods, especially hybrid methods, can be employed. In this paper, a semi-supervised deep hybrid multi-task model (DHMTM) for RUL prediction is developed. DHMTM contains two temporal models for unlabeled and labeled multivariate time series data, respectively. In the model training process, adding an extra task of prediction of future sensor signal values, DHMTM can obtain HIs which improve the RUL prediction accuracy. Besides, temporal dependency of sensor signals is captured in the proposed DHMTM. The effectiveness of the proposed model is validated using the C-MAPSS and the lithium-ion batteries datasets. The results show that using the proposed method, the prediction errors for the two datasets have been reduced by 2.5% and 23.5% on average, respectively, compared to the fully supervised regression model, and 17% and 44%, respectively, on average compared to three other widely-used semi-supervised methods.