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

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

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.

Suggested Citation

  • Xiang, Sheng & Qin, Yi & Luo, Jun & Pu, Huayan & Tang, Baoping, 2021. "Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004439
    DOI: 10.1016/j.ress.2021.107927
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    References listed on IDEAS

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