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

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  • Chaleshtori, Amir Eshaghi
  • Aghaie, Abdollah

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.

Suggested Citation

  • Chaleshtori, Amir Eshaghi & Aghaie, Abdollah, 2024. "A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
  • Handle: RePEc:eee:reensy:v:242:y:2024:i:c:s0951832023006348
    DOI: 10.1016/j.ress.2023.109720
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    References listed on IDEAS

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    1. Ma, Yulin & Li, Lei & Yang, Jun, 2022. "Convolutional kernel aggregated domain adaptation for intelligent fault diagnosis with label noise," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    2. Liu, Yi & Xiang, Hang & Jiang, Zhansi & Xiang, Jiawei, 2023. "Second-order transient-extracting S transform for fault feature extraction in rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    4. Guo, Junchao & He, Qingbo & Zhen, Dong & Gu, Fengshou & Ball, Andrew D., 2023. "Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Liu, Zhao-Hua & Chen, Liang & Wei, Hua-Liang & Wu, Fa-Ming & Chen, Lei & Chen, Ya-Nan, 2023. "A Tensor-based domain alignment method for intelligent fault diagnosis of rolling bearing in rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Melani, Arthur Henrique de Andrade & Michalski, Miguel Angelo de Carvalho & da Silva, Renan Favarão & de Souza, Gilberto Francisco Martha, 2021. "A framework to automate fault detection and diagnosis based on moving window principal component analysis and Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
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    1. Tian, Jilun & Jiang, Yuchen & Zhang, Jiusi & Luo, Hao & Yin, Shen, 2024. "A novel data augmentation approach to fault diagnosis with class-imbalance problem," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Li, Xin & Li, Shuhua & Wei, Dong & Si, Lei & Yu, Kun & Yan, Ke, 2024. "Dynamics simulation-driven fault diagnosis of rolling bearings using security transfer support matrix machine," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Yu, Aobo & Cai, Bolin & Wu, Qiujie & García, Miguel Martínez & Li, Jing & Chen, Xiangcheng, 2024. "Source-free domain adaptation method for fault diagnosis of rotation machinery under partial information," Reliability Engineering and System Safety, Elsevier, vol. 248(C).

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