A lightweight GAN-based fault diagnosis method based on knowledge distillation and deep transfer learning Article Swipe
Hongyu Zhong
,
Samson S. Yu
,
Hieu Trinh
,
Rui Yuan
,
Yong Lv
,
Yanan Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad0fd2
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad0fd2
Generative adversarial networks (GANs) have shown promise in the field of small sample fault diagnosis. However, it is worth noting that generating synthetic data using GANs is time-consuming, and synthetic data cannot fully replace real data. To expedite the GAN-based fault diagnostics process, this paper proposes a hybrid lightweight method for compressing GAN parameters. First, three modules are constructed: a teacher generator, a teacher discriminator, and a student generator, based on the knowledge distillation GAN (KD-GAN) approach. The distillation operation is applied to both teacher generator and student generator, while adversarial training is conducted for the teacher generator and the teacher discriminator. Furthermore, a joint loss function is proposed to update the parameters of the student generator by combining distillation loss and adversarial loss. Additionally, the proposed KD-GAN method is combined with deep transfer learning (DTL) and leverages real data to enhance the diagnostic model’s performance. Two numerical experiments are performed to demonstrate that the proposed KD-GAN-DTL method outperforms other GAN-based fault diagnosis methods in terms of computational time and diagnostic accuracy.
Related Topics
Concepts
Discriminator
Computer science
Generator (circuit theory)
Distillation
Fault (geology)
Process (computing)
Generative adversarial network
Artificial intelligence
Algorithm
Computer engineering
Deep learning
Power (physics)
Programming language
Chemistry
Quantum mechanics
Physics
Organic chemistry
Geology
Seismology
Telecommunications
Detector
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6501/ad0fd2
- OA Status
- hybrid
- Cited By
- 12
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388965959
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388965959Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6501/ad0fd2Digital Object Identifier
- Title
-
A lightweight GAN-based fault diagnosis method based on knowledge distillation and deep transfer learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-24Full publication date if available
- Authors
-
Hongyu Zhong, Samson S. Yu, Hieu Trinh, Rui Yuan, Yong Lv, Yanan WangList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6501/ad0fd2Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1361-6501/ad0fd2Direct OA link when available
- Concepts
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Discriminator, Computer science, Generator (circuit theory), Distillation, Fault (geology), Process (computing), Generative adversarial network, Artificial intelligence, Algorithm, Computer engineering, Deep learning, Power (physics), Programming language, Chemistry, Quantum mechanics, Physics, Organic chemistry, Geology, Seismology, Telecommunications, DetectorTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 5Per-year citation counts (last 5 years)
- References (count)
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39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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