Dynamic predictive maintenance strategy for system remaining useful life prediction via deep learning ensemble method

In data-driven prognostics and health management (PHM), most studies focus only on prognostics performance but rarely consider maintenance decision problems. However, simple predictive maintenance decisions are not effective in dealing with the complex operating conditions faced in modern industrial systems. Thus, we propose a complete data-driven dynamic predictive maintenance strategy for system remaining useful life (RUL) prediction via deep learning ensemble method to solve this problem. This deep learning ensemble method is composed of a convolutional neural network (CNN) and a bidirectional long short-term memory network (Bi-LSTM), which aims to effectively predict the system RUL. Then, we consider a dynamic predictive maintenance strategy with uncertain system mission cycles based on the RUL predicted by deep learning ensemble method. Meanwhile, this dynamic predictive maintenance strategy includes order, stock, and maintenance decisions. In addition, the number of missions performed by the system and the reliability of the last performed mission are presented based on the mission cycle and the predicted RUL. Finally, experimental results from the NASA turbofan engine dataset C-MAPSS show the favorable performance of the proposed dynamic predictive maintenance strategy compared to the existing maintenance strategy.