A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis

Amir Eshaghi Chaleshtori and Abdollah Aghaie

Reliability Engineering and System Safety, 2024, vol. 242, issue C

Abstract: 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.

Keywords: Bearing fault diagnosis; Feature selection; Conditional entropy; Gaussian Mixture Model; Weighted principal component analysis (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832023006348
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:242:y:2024:i:c:s0951832023006348

DOI: 10.1016/j.ress.2023.109720

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().