Abstract
The rolling bearing is an essential element widely used in the rotating machinery. Bearing failures are among the main reasons for breakdown of rotating machinery. Therefore, fault detection of bearing is necessary to reduce the probability of breakdown and safety accidents. A novel fault diagnosis method for rolling bearing based on Refined Composite Multiscale Dispersion Entropy (RCMDE) and Improved Support Vector Machine (ISVM) is presented in this paper. The RCMDE is a new irregular index in biomedical signal analysis, which has lower computational cost and more stable results. Therefore, the RCMDE is introduced as fault feature to represent the bearing fault characteristics. After feature extraction, an improved support vector machine based on whale optimization algorithm (WOA) and support vector machine (SVM) is proposed as a fault classifier, which has the advantages of less training samples and good classification effect. The effectiveness of the proposed method in bearing fault diagnosis is verified by using bearing fault experimental data.
Highlights
The rolling bearing plays an important role in rotating machinery and transmission machinery system
Aiming at the shortages of above methods, an intelligent fault diagnosis method for rolling bearings based on refined composite multiscale dispersion entropy (RCMDE) and improved support vector machine (ISVM) is proposed in this paper
In order to improve the classification effect of traditional support vector machine, this paper proposes an improved support vector machine whose parameters are optimized by whale optimization algorithm (WOA)
Summary
The rolling bearing plays an important role in rotating machinery and transmission machinery system. Aiming at the shortages of above methods, an intelligent fault diagnosis method for rolling bearings based on refined composite multiscale dispersion entropy (RCMDE) and improved support vector machine (ISVM) is proposed in this paper. Compared with multi-scale sample entropy [24] and multi-scale fuzzy entropy [25], RCMDE has lower computational cost and more stable results For these reasons, the RCMDE is a simple and effective feature extraction method which has more potential to be applied in engineering practice. The improved support vector machine (ISVM) based on whale optimization algorithm (WOA) [26] and support vector machine (SVM) is proposed as fault classifier for rolling bearings.
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