Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction

Sheng Xiang, Yi Qin, Jun Luo, Huayan Pu and Baoping Tang

Reliability Engineering and System Safety, 2021, vol. 216, issue C

Abstract: The prediction of aero-engine remaining useful life (RUL) is helpful for its operation and maintenance. Aiming at the challenge that most neural networks (NNs), including long short-term memory (LSTM), cannot process the input data in different update modes based on its importance degree, a novel variant of LSTM named multicellular LSTM (MCLSTM) is constructed. The level division unit is proposed to determine the importance degree of input data, and then multiple cellular units are designed to update the cell states according to the data level. Thus, MCLSTM can well mine different levels of degradation trends. Based on MCLSTM and a deep NN (DNN), a deep learning model for RUL prediction is set up, where MCLSTM and a branch of the DNN is used to extract health indicators (HIs) of aero-engine from raw data, and the other part of the DNN is applied to generate the HIs from human-made features and predict the RUL based on the concatenated HIs. The proposed RUL prediction model is successfully applied to predict the RULs of aero-engines via the Commercial Modular Aero Propulsion System Simulation datasets, and the comparative results show that it has a better comprehensive prediction performance than the commonly-used machine learning methods.

Keywords: RUL prediction; Degradation trend; Multi-resource data; Health feature; Data level (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004439

DOI: 10.1016/j.ress.2021.107927

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