Abstract
Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy (RCMRDE) and random forest is developed. Firstly, rolling bearing vibration signals are adaptively decomposed by variational mode decomposition (VMD), and then the RCMRDE values of 25 scales are calculated for original signal and each decomposed component as the initial feature set. Secondly, based on the joint mutual information maximization (JMIM) algorithm, the top 15 sensitive features are selected as a new feature set and feed into random forest model to identify bearing health status. Finally, to verify the effectiveness and superiority of the presented method, actual data acquisition and analysis are performed on the bearing fault diagnosis experimental platform. These results indicate that the presented method can precisely diagnose bearing fault types and damage degree, and the average identification accuracy rate is 97.33%. Compared with the refine composite multiscale dispersion entropy (RCMDE) and multiscale dispersion entropy (MDE), the fault diagnosis accuracy is improved by 2.67% and 8.67%, respectively. Furthermore, compared with the RCMRDE method without VMD decomposition, the fault diagnosis accuracy is improved by 3.67%. Research results prove that a better feature extraction technique is proposed, which can effectively overcome the deficiency of existing entropy and significantly enhance the ability of fault identification.
Highlights
As an important component of large-scale electromechanical equipment, the rolling bearing health status is critical for the stable operation of equipment
In [15], variational mode decomposition (VMD) and energy entropy are combined for rolling bearing fault detection, which has a better effect than empirical mode decomposition (EMD) and wavelet transform (WT)
The joint mutual information maximization (JMIM) is an effective feature selection algorithm, which can extract features and create a feature subset efficiently based on joint mutual information [44]
Summary
As an important component of large-scale electromechanical equipment, the rolling bearing health status is critical for the stable operation of equipment. In [15], VMD and energy entropy are combined for rolling bearing fault detection, which has a better effect than EMD and WT. Inspired by refined composite multiscale, the refined composite multiscale reverse dispersion entropy (RCMRDE) is proposed in this article, which can mine rolling bearing fault information comprehensively. Considering the excellent performance of RF, in order to accurately mine fault information from bearing monitoring data and achieve high precision fault pattern recognition, an intelligent fault diagnosis method integrating VMD decomposition, refine composite multiscale reverse dispersion entropy (RCMRDE), JMIM feature selection and RF is proposed. In. Section 4, different rolling bearing health signals are collected, and feature set construction based on VMD and RCMRDE and feature selection method based on JMIM algorithm are discussed.
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