Remaining useful life prediction method combining the life variation laws of aero-turbofan engine and auto-expandable cascaded LSTM model

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The real-time status monitoring and health management of aero-turbofan engines (ATEs) can effectively reduce the risk of engine failure and ensure aircraft flight safety. Accurate prediction of the remaining useful life (RUL) of ATE is a vital tool for effectively monitoring the engine operating condition, in which long-short term memory (LSTM) networks are often applied to RUL prediction. However, because of the complex mechanical structure and operation mode of the aero-engine, the prediction accuracy of the LSTM network is not enough to meet the actual demand. This paper proposes an auto-expandable cascaded long-short term memory (ACLSTM) prediction model that incorporates the lifetime variation laws of ATE, which is mainly applied for the degradation assessment of ATEs and the accurate prediction of RUL. The ACLSTM model adopts the network structure of multiple LSTM modules connected step by step to continuously set the prediction error of the previous module as the training outputs of the latter module. This data processing method transforms the prediction process of the original data into that of the output error, effectively reducing the prediction error and improving the prediction effect. In addition, to further improve the prediction accuracy, this paper comprehensively proposes several empirical formulas for further correction of the prediction effect obtained by the ACLSTM model. In the experimental part, the prediction effectiveness of the proposed method is tested based on four subsets of the C-MAPSS dataset published by the National Aeronautics and Space Administration. The experimental results on the four datasets show that the root mean square error (RMSE) of the ACLSTM prediction model decreases by 95.44% on average compared to the traditional LSTM network. In addition, the RMSE of the model decreases by 96.48% on average after incorporating the empirical formula. The proposed method has the lowest RMSE compared to other methods with the highest prediction accuracy. The experiments thoroughly verify that the ACLSTM model and the proposed empirical formula are feasible and effective for improving the prediction accuracy of the RUL of ATE.