Aero Engine Life Prediction Based on Multi Scale Temporal Convolutional Networks

The remaining useful life (RUL) of the aero engine is important for the safe operation of the engine equipment and the development of maintenance plans. At present, the existing methods are difficult to effectively extract the degradation features of equipment under complex operating conditions and complex faults. To solve this problem, an engine RUL prediction method based on multi scale temporal convolutional network (MTCN) is proposed. In this method, time convolutional networks are used to extract temporal information. Moreover, the degradation features of equipment under complex operating conditions are extracted by multi scale convolution kernel. As a result, it is better to predict the RUL of equipment under extreme conditions. To verify the validity of the proposed method, abundant experiments are carried out on the C MAPSS dataset. The results show that the proposed method can effectively improve the accuracy of RUL prediction under complex conditions.