Remaining Useful Life Prediction in Varied Operational Conditions Considering Change Point: A Novel Deep Learning Approach with Optimum Features

In the era of Internet of Things (IoT), remaining useful life (RUL) prediction of turbofan engines is crucial. Various deep learning (DL) techniques are proposed recently to predict RUL for such systems, have remained silent on the effect of environmental changes on machine reliability. This paper has proposed three-fold aims, (i) to identify the change point in RUL trend and pattern (ii) to select most relevant features, and (iii) to predict RUL with the selected features and identified change point. A two-stage feature selection algorithm was developed, followed by a change point identification mechanism and finally, a Bi-directional long short-term memory (BiLSTM) model has been designed to predict RUL. The study utilizes NASA’s C-MAPSS dataset to check the performance of the proposed methodology. The findings affirm that the proposed method enhances the stability of DL models, resulting in an approximate 30% improvement in RUL prediction compared to popular and cutting-edge DL models.