Granularity knowledge-sharing supervised contrastive learning framework for long-tailed fault diagnosis of rotating machinery-Reference-Cited by-同舟云学术

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Granularity knowledge-sharing supervised contrastive learning framework for long-tailed fault diagnosis of rotating machinery

Published:2024-10 Issue: Volume:301 Page:112354
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Chang Shuyuan,Wang Liyong,Shi Mingkuan,Zhang Jinle,Yang Li

Funder

National Defense Basic Scientific Research Program of China

National Aerospace Science Foundation of China

Basic Scientific Fund for National Public Research Institutes of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference31 articles.

1. Multi-fault diagnosis of industrial rotating machines using data-driven approach: a review of two decades of research;Gawde;Eng. Appl. Artif. Intell.,2023

2. Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine;Han;Inf. Sci.,2023

3. Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization;Yan;Reliab. Eng. Syst. Saf.,2023

4. Deep continual transfer learning with dynamic weight aggregation for fault diagnosis of industrial streaming data under varying working conditions;Li;Adv. Eng. Informat.,2023

5. Industrial surface defect detection and localization using multi-scale information focusing and enhancement GANomaly;Peng;Expert Syst. Appl.,2024

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