Fault prediction of bearings based on LSTM and statistical process analysis

Junqiang Liu, Chunlu Pan, Fan Lei, Dongbin Hu and Hongfu Zuo

Reliability Engineering and System Safety, 2021, vol. 214, issue C

Abstract: Aero-engine bearing is the key component of aero-engine load-bearing transmission system, and its fault will affect the reliability and safety of aero-engine system, so it is particularly important to predict the fault of aero-engine bearings. The previous bearing researches are mainly based on the single-stage model, and the regression neural network has the problem of gradient disappearance, which leads to the uncertainty of prediction. Therefore, this paper proposes a novel model named LSS which combines the advantages of long short-term memory (LSTM) network with statistical process analysis to predict the fault of aero-engine bearings with multi-stage performance degradation. An algorithm based on the proposed model is put forward. Firstly, the time feature of bearing vibration signal is extracted and divided into multi-stage signals by statistical process analysis. Then, the multi-stage signals are input into LSS for prediction. The bearing datasets published by NASA and FEMTO-ST institute are used to prove the effectiveness of proposed method. The results show that the proposed method has higher prediction accuracy than recurrent neural network (RNN), support vector regression (SVR) and LSTM. Therefore, this method can divide the level of fault to reduce uncertainty, so as to improve prediction accuracy of the performance degradation trend and the RUL of bearings.

Keywords: Rolling bearing; Fault prediction; Long short-term memory network; Statistical process analysis; Multi-stage; Performance degradation (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021001873

DOI: 10.1016/j.ress.2021.107646

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