Author(s)

Jie Shen ¹

Affiliation(s)

¹ School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Corresponding Author

Jie Shen

ABSTRACT

This paper addresses the difficulty that decision-level fusion in unsupervised fault diagnosis of rolling bearing multi-source data fails to effectively exploit the complementary information among multi-source data. A rolling bearing fault diagnosis method based on multi-view clustering (MVC) is proposed. First, a variational autoencoder (VAE) is employed to extract latent features from multi-source signals. On this basis, a joint matrix that integrates multi-source data features and a high-confidence matrix containing consistency information are constructed. A low-rank approximation method is then used to mine complementary information within multi-source information and enhance structural consistency. Finally, accurate identification of unsupervised fault diagnosis is achieved. Experimental results show that the proposed method can effectively utilize the complementary information among multiple views, overcome dependence on a single data source, and exhibit excellent robustness and stability.

KEYWORDS

Variational autoencoder; Multi-view clustering; Rolling bearing; Fault diagnosis

CITE THIS PAPER

Jie Shen. Multi-Source Information Fault Diagnosis Method for Rolling Bearing Based on Multi-View Clustering. Journal of Engineering Mechanics and Machinery (2026). Vol. 11, No. 1, 180-192. DOI: http://dx.doi.org/10.23977/jemm.2026.110117.

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Sponsors, Associates, and Links

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• Cybernetics and Mechatronics
  
  
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• Mechanical Vibration and Noise