Prediction of Aero-Engine Remaining Useful Life Combined with Fault Information

Since the fault information of an aero-engine is very important for the remaining useful life of an aero-engine, the paper proposes to combine the fault information for the remaining useful life prediction of an aero-engine. Firstly, we preprocessed the signals of the dataset. Next, the preprocessed signals were used to train a CNN (convolutional neural network)-based fault diagnosis model and obtain fault features from the model. Then, we combined BIGRU (bidirectional gated recurrent unit) and the fault features to predict the remaining useful life of the aero-engine. We used the CMAPSS (commercial modular aviation propulsion system simulation) dataset to verify the effectiveness of the proposed method. After that, comparison experiments with different parameters, structures, and models were conducted in the paper.