A deep learning framework for sensor-equipped machine health indicator construction and remaining useful life prediction

Prognostic and health management (PHM) effectively reduces the economic loss of sensor-equipped machine downtime caused by under-maintenance and the waste of resources resulted from over-maintenance. The remaining useful life (RUL) prediction is the most critical step in PHM. However, accurate RUL prediction in a multiple sensors data environment faces challenges and difficulties. In this paper, we firstly consider the change point where the sensor-equipped machine drifts from a health state (initial stage) to the degradation stage, and combine the deep learning model with the change point to construct the health indicator (HI). Then, a long short-term memory model combined with an attention mechanism (LSTM_Att) is used to iteratively predict the future HI. Additionally, the predicted RUL distribution of the studied sensor-equipped machine is estimated using the similarity method based on the HI data of historical multiple sensor-equipped machines. Then, the confidence interval of RUL is obtained. Finally, the proposed method is verified on the publicly available turbofan engine degradation data set. The experimental results show that the proposed method outperforms the state-of-art benchmark methods. • A deep learning model with change point recognition is used to construct a health indicator. • A method is proposed to predict the future health indicator. • The sensor-equipped machine’s RUL distribution and confidence interval are estimated.