Abstract
Aiming at the problem of fault diagnosis, a novel method based on dictionary learning and parameter-optimized Support Vector Machine (SVM) was proposed in this paper and applied it to bearing fault diagnosis. Firstly, the collected bearing fault signals are transformed into gray images after data processing. Then, using dictionary learning, the gray images are denoised and output them as signal data. Finally, the SVM multi-classification model obtained by using Grid Search (GS) algorithm to optimize penalty parameter c and kernel function parameter g is used to classify and identify the fault type. This paper is based on data from Case Western Reserve University Bearing Center for experimental verification. The results show that the proposed model can continuously achieve the accuracy of 100% in the process of bearing fault diagnosis in different environments, which proves that the proposed method can accurately and effectively realize the fault diagnosis classification of bearings.
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