Self-supervised fusion of deep soft assignments for multi-view diagnosis of machine faults

Chuan Li (), Yifan Wu (), Manjun Xiong (), Shuai Yang () and Yun Bai ()
Additional contact information
Chuan Li: Chongqing Technology and Business University
Yifan Wu: Chongqing Technology and Business University
Manjun Xiong: Chongqing Technology and Business University
Shuai Yang: Chongqing Technology and Business University
Yun Bai: Chongqing Technology and Business University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 4, No 14, 2493-2507

Abstract: Abstract Fault patterns are often unavailable for machine fault diagnosis without prior knowledge. This makes it challenging to diagnose the existence of machine faults and their types. To address this issue, a novel scheme of deep soft assignments fusion network (DSAFN) is proposed for the self-supervised multi-view diagnosis of machine faults. To enhance the robustness of the model and prevent overfitting, random noise is added to the collected signals. In each view, vibration features are extracted by a denoising autoencoder. Using the extracted deep features, a soft assignment fusion strategy is proposed to fully utilize both the public and complementary information of multiple views. Critical diagnosis missions, including novel fault detection and fault clustering, are accomplished through binary clustering and multi-class clustering of DSAFN, respectively. Two diagnostic experiments are conducted to validate the proposed method. The results indicate that the proposed method performs better than state-of-the-art peer methods in terms of diagnostic accuracy and noise robustness.

Keywords: Machine fault; Multi-view diagnosis; Denoising autoencoder; Self-supervised fusion; Deep soft assignment (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02360-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02360-z

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02360-z

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().