A Hybrid Method of Remaining Useful Life Prediction for Aircraft Auxiliary Power Unit

The aircraft auxiliary power unit (APU) is mainly used to provide electricity and compressed air to the aircraft. Not only can it help to start main engines, but also it can provide essential thrust for the emergency landing. It is required to be reliable as high as possible when it is installed in the aircraft. However, the reality is that the degradation of APU is a nonlinear process. In this term, only a data-driven method or a physics-based method can hardly make accurate prediction for the remaining useful life (RUL) of the aircraft APU. Therefore, a hybrid RUL prediction method is proposed by fusing an artificial intelligence-based model and a physics-based model in this article. To be specific, Wiener process is used to formulate a physical model for generating degradation data. Then, the generated data and the corrected data are both utilized as the input of long short-term memory (LSTM) to enhance RUL prediction of the aircraft APU. The contributions of this study include: 1) A hybrid method for RUL prediction of the aircraft APU is proposed. 2) The influences of ambient environment and training data dependency of LSTM are solved. 3) The proposed method provides a novel RUL prediction method for the on-wing aircraft APU, which is evaluated by the real data.