An aero-engine remaining useful life prediction model based on feature selection and the improved TCN

Abstract

Inferring the remaining useful life (RUL) of an aero-engine based on complex data from aircraft sensors is one of the important issues to ensure flight safety. To this end, this paper is intended to propose a RUL prediction model based on the feature extraction method and the improved temporal convolution network (TCN). First, the XGBoost (eXtreme Gradient Boosting) model is used to assess the importance of the data and filter the features base on the resulting correlation. Then, the RUL prediction model is constructed by paralleling TCN networks with different expansion rates, which expands the receptive field and further improves the information obtained by the network from the data. Moreover, the network is further optimized with dynamic hyperparameter search methods. Finally, through comparative experiments, the proposed prediction model is evaluated based on the turbofan aero-engine operation failure prediction benchmark dataset (CMAPSS). The experimental results show that by deleting some data with low correlation, the proposed model can achieve better prediction accuracy, which is superior to other mainstream models in the references.

Keywords

• CMAPSS
• Deep learning
• TCN
• Time series
• Remaining useful life