Remaining useful life prediction for complex systems considering varying future operational conditions

Remaining useful life (RUL) prediction technology is important for optimizing maintenance schedules. With the advancement of sensing technology, several deep learning approaches have been proposed to predict RUL without relying on prior knowledge about systems. However, previous deep learning-based approaches rarely consider the future operational conditions, which can be known according to the future work plan and is an important influential factor for RUL prediction. This paper proposes a multi-input neural network based on long short-term memory for RUL prediction considering the temporal dependencies among the measurements when the future operational conditions are known. The sliding window approach is employed for determining the input time sequences of previous monitoring data (including operational condition and sensor measurements), and the length of input time sequences of the future operational conditions are determined based on the prior estimated RUL. Fine-tuning strategy is proposed to make the training of the multi-input network more effective. To illustrate the effectiveness of the proposed methods, a case study referring to the C-MAPSS dataset is used and a sensitivity analysis is also conducted on the future operational conditions.