期刊
RELIABILITY ENGINEERING & SYSTEM SAFETY
卷 242, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2023.109720
关键词
Bearing fault diagnosis; Feature selection; Conditional entropy; Gaussian Mixture Model; Weighted principal component analysis
This paper proposes a novel approach that combines weighted principal component analysis with the Gaussian mixture model for bearing fault diagnosis. The method utilizes feature selection and the conditional entropy divergence indicator to enhance the efficiency and accuracy of fault diagnosis. Experimental results demonstrate the superior performance of this method compared to alternative techniques, achieving high accuracy rates in different case studies.
The efficient diagnosis of bearing faults requires the extraction of informative features. This paper presents a novel approach that combines Weighted Principal Component Analysis (WPCA) with the Gaussian Mixture Model (GMM) for bearing fault diagnosis. The method employs GMM as a fault classifier, aiming to enhance both efficiency and diagnostic accuracy. The proposed algorithm, Expectation Selection Maximization (ESM), introduces a feature selection step to identify the most relevant features for effective bearing fault detection. Specifically, the suggested algorithm utilizes the conditional entropy divergence indicator, a statistical metric, to quantify the significance of features in detecting bearing faults. To validate the effectiveness of this approach, two distinct case studies are conducted using datasets obtained from the University of Ottawa and Case Western Reserve University (CWRU). These datasets encompass a wide range of bearing working conditions, providing a comprehensive evaluation. Experimental results underscore the merits of the approach, achieving an average accuracy rate of 93% for the University of Ottawa dataset and 80% for the CWRU dataset. Furthermore, the findings highlight the superior performance of the proposed method compared to alternative techniques, as evidenced by the receiver operating characteristic (ROC) curve metric.
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