A remaining useful life prediction method based on LSTM-DCGAN for aero-engines

Abstract

Abstract Turbofan engine is a key component in aerospace. Its health condition determines whether an aircraft can operate reliably. However, it is difficult to predict the remaining useful life (RUL) precisely because of the characteristics of complex operating conditions and various failure modes. To predict the RUL more accurately and make full use of the advantages of neural networks, a RUL prediction model based on a long short-term memory network (LSTM) and deep convolutional generative adversarial network (DCGAN) is proposed and called LSTM-DCGAN in this paper. In the proposed LSTM-DCGAN, DCGAN is used to obtain knowledge of the training dataset, then the generator after pretraining in the DCGAN is attached after an LSTM network for further feature extraction. The effectiveness of the proposed LSTM-DCGAN is validated on the C-MAPSS aero-engine degradation dataset and compared with other methods.