Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine

Abstract Rolling bearing fault diagnosis is an important and time sensitive task, to ensure the normal operation of rotating machinery. This paper proposes a fault diagnosis for rolling bearings, based on Generalized Refined Composite Multiscale Sample Entropy (GRCMSE), Supervised Isometric Mapping (S-Isomap) and Grasshopper Optimization Algorithm based Support Vector Machine (GOA-SVM). First, GRCMSE is utilized to characterize the complexity of vibration signals, at different scales. Furthermore, an effective manifold learning algorithm, named S-Isomap, is applied, to compress the high-dimensional feature set into a low-dimensional space. Subsequently, GOA-SVM classifier is proposed for pattern recognition, having higher recognition accuracy than other classifiers. The performance of the proposed method has been verified by its successful application in a rolling bearing fault diagnosis experiment. Compared with the existing methods, this approach improves the classification accuracy to 100%. The produced results indicate that the proposed method can effectively detect bearing faults, maintaining high accuracy.

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