Early fault prediction method combining SWDAE and LSTM for rolling bearings based on Bayesian optimization

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (18) : 286-297.

SHI Huaitao1, SHANG Yajun1, BAI Xiaotian1, GUO Lei1, MA Hui2

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Abstract

The early fault characteristics of rolling bearings are usually rather weak and it is difficult to effectively extract under the background of strong noise so that the life cycle is difficult to accurately predict.Aiming at this,an initial fault prediction model based on Bayesian optimization(BO) consisting of sliding window stacked denoising auto encoder (SWDAE) and long short-term memory(LSTM) network was proposed.The sliding window algorithm was used to retain historical normal data with non-linear characteristics and time series characteristics, so,when the processed data were input into the model for training, the model was able to learn the normal running state trend of the rolling bearing.The data of the rolling bearing operation were then input into the trained SWDAE-LSTM model for real-time online monitoring, and the residual of the predicted value and the true value of the model was used to detect the early failure of the rolling bearing.Aiming at the difficulty in selecting the hyperparameter combination of the model, a Bayesian optimization algorithm was used to tune the hyperparameter of the model.Finally, the bearing full life cycle data from the University of Cincinnati Intelligent Maintenance Center (IMSCenter) and the data from a mechanical failure integrated simulation experiment device were used for simulation verification.The results show that the model of intelligent parameter adjustment using Bayesian optimization algorithm and the method based on time domain index can detect the early fault of the rolling bearing effectively and have strong robustness.Compared with other deep learning methods, the diagnostic accuracy of the model is higher than that of other methods, which further proves its validity and reliability.

Key words

rolling bearing / initial fault diagnosis / Bayesian optimization(BO) / sliding window algorithm / stacked denoising auto encoder(SDAE) / long short-term memory(LSTM) network

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SHI Huaita, SHANG Yajun, BAI Xiaotian, GUO Lei, MA Hui. Early fault prediction method combining SWDAE and LSTM for rolling bearings based on Bayesian optimization[J]. Journal of Vibration and Shock, 2021, 40(18): 286-297

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