Bearing fault diagnosis based on TVF-EMD decomposition and random forest prediction

Binjie Chang; Ruimin Cui

doi:10.1117/12.3006694

26 September 2023 Bearing fault diagnosis based on TVF-EMD decomposition and random forest prediction

Binjie Chang, Ruimin Cui

Proceedings Volume 12793, International Conference on Mechatronics and Intelligent Control (ICMIC 2023); 127931D (2023) https://doi.org/10.1117/12.3006694
Event: International Conference on Mechatronics and Intelligent Control (ICMIC2023), 2023, Wuhan, China

Abstract

In view of the characteristics of non-stationary signals in bearings under abnormal conditions, a new diagnosis method for bearing fault detection was proposed based on TVF-EMD, kurtosis selection and envelope entropy as energy extraction method and random forest classification model. First, TVF-EMD method is used to decompose the initial signal, and the decomposed intrinsic modal components (IMFS) are obtained. Secondly, several natural modal components which can best represent the original signal are selected by the kurtosis values of the natural modal components. Secondly, the envelope entropy is used as the energy extraction method. Finally, the stochastic forest classification model is used to train and predict the original data. After comparing the test data with the original data, the accuracy rate reached 99.5 percent.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Binjie Chang and Ruimin Cui "Bearing fault diagnosis based on TVF-EMD decomposition and random forest prediction", Proc. SPIE 12793, International Conference on Mechatronics and Intelligent Control (ICMIC 2023), 127931D (26 September 2023); https://doi.org/10.1117/12.3006694

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