Temporal Convolutional Networks Applied to Remaining Useful Life Prediction of Turbofan Engine

Temporal Convolutional Networks Applied to Remaining Useful Life Prediction of Turbofan Engine

In recent years, turbofan engine failures have frequently occurred, traditional breakdown maintenance has been difficult to meet the demand. Remaining useful life (RUL) prediction technology has become one of the effective ways to solve the above-mentioned problems. To accurately obtain the RUL of the turbofan engine, an RUL prediction method based on Temporal Convolutional Networks (TCN) is proposed in this paper. The overall network can be divided as follows: Firstly, combining the advantages of LSTM and autoencoder to complete the feature extraction of sequence data. Secondly, TCN is used in the RUL prediction part. TCN does not disclose future sequence information and it has a larger and more flexible receptive field. TCN also features the residual structure to make full use of the original input information and to avoid the disappearance of gradients. The effectiveness of the proposed method is verified in CMAPSS datasets. Finally, compared with other excellent RUL prediction methods, the proposed method improves the prediction accuracy on complex datasets.