Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Rolling Element Bearing Fault Time Series Prediction Using Optimized MCKD-LSTM Model

Version 1 : Received: 20 November 2021 / Approved: 22 November 2021 / Online: 22 November 2021 (10:55:00 CET)

How to cite: Ma, L.; Jiang, H.; Ma, T.; Zhang, X.; Xia, L.; Zeng, H.; Jiang, Y.; Shen, Y. Rolling Element Bearing Fault Time Series Prediction Using Optimized MCKD-LSTM Model. Preprints 2021, 2021110377 (doi: 10.20944/preprints202111.0377.v1). Ma, L.; Jiang, H.; Ma, T.; Zhang, X.; Xia, L.; Zeng, H.; Jiang, Y.; Shen, Y. Rolling Element Bearing Fault Time Series Prediction Using Optimized MCKD-LSTM Model. Preprints 2021, 2021110377 (doi: 10.20944/preprints202111.0377.v1).

Abstract

This paper realizes early bearing fault warning through bearing fault time series prediction, and proposes a bearing fault time series prediction model based on optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to ensure bearings operation reliability. The model is based on lifecycle vibration signal of the bearing, to begin, the cuckoo search (CS) is utilized to optimize the parameter filter length L and deconvolution period T of MCKD, taking into account the influence and periodicity of the bearing time series, the fault impact component of the optimized MCKD deconvolution time series is improved. Then select the LSTM learning rate α depending on deconvolution time series. Finally, the dataset obtained through various preprocessing approaches are used to train and predict the LSTM model. The average prediction accuracy of the optimized MCKD-LSTM model is 26 percent higher than that of the original time series, proving the efficiency of this method, and the prediction results track the real fault data well, according to the XI'AN JIAOTONG University XJTU-SY bearing dataset.

Keywords

deep learning; time series prediction; long short-term memory; recurrent neural network; maximum correlation kurtosis deconvolution; cuckoo search.

Subject

ENGINEERING, Mechanical Engineering

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.