CycleGAN Based Unsupervised Domain Adaptation for Machine Fault Diagnosis Article Swipe
Naibedya Pattnaik
,
Uday Sai Vemula
,
Kriti Kumar
,
Anshul Kumar
,
Angshul Majumdar
,
M Girish Chandra
,
Arpan Pal
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1145/3560905.3568303
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1145/3560905.3568303
Fault diagnosis plays a vital role in ensuring the normal operation of the machine and safe production. In recent years, data-driven techniques have gained a lot of popularity for machine fault diagnosis. But most of these techniques assume the training and test data have the same distribution. However, in most practical application scenarios, domain discrepancy can be observed between the training (source) and test (target) data due to different factors like changes in the operating conditions, different sensor locations, etc. Classical approaches fail to address such domain discrepancy, which leads to poor performance. The problem becomes more challenging when the target is completely unlabeled. To address this scenario, domain adaptation techniques are used to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Recently, adversarial network based domain adaptation has been extensively explored for fault diagnosis. But the adversarial loss alone does not guarantee the translation of the source to the desired target domain (class consistent). Here, we propose to use cycle-consistency loss employing 1D-CycleGAN for learning the source to target mapping for unsupervised adaptation for bearing fault diagnosis. The proposed method is evaluated for two different scenarios, with the source and target from (i) same machine but different working conditions and (ii) different but related machines. Experimental results show that while the proposed method performs comparable to the best-performing benchmark for the first case, it significantly outperforms all the state-of-the-art methods for the challenging second case.
Related Topics
Concepts
Computer science
Fault (geology)
Artificial intelligence
Benchmark (surveying)
Machine learning
Domain (mathematical analysis)
Test data
Consistency (knowledge bases)
Adversarial system
Domain adaptation
Adaptation (eye)
Reliability (semiconductor)
Pattern recognition (psychology)
Optics
Classifier (UML)
Programming language
Power (physics)
Mathematics
Geodesy
Geology
Mathematical analysis
Physics
Quantum mechanics
Geography
Seismology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3560905.3568303
- https://dl.acm.org/doi/pdf/10.1145/3560905.3568303
- OA Status
- gold
- Cited By
- 1
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4317926988
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4317926988Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3560905.3568303Digital Object Identifier
- Title
-
CycleGAN Based Unsupervised Domain Adaptation for Machine Fault DiagnosisWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-06Full publication date if available
- Authors
-
Naibedya Pattnaik, Uday Sai Vemula, Kriti Kumar, Anshul Kumar, Angshul Majumdar, M Girish Chandra, Arpan PalList of authors in order
- Landing page
-
https://doi.org/10.1145/3560905.3568303Publisher landing page
- PDF URL
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https://dl.acm.org/doi/pdf/10.1145/3560905.3568303Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3560905.3568303Direct OA link when available
- Concepts
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Computer science, Fault (geology), Artificial intelligence, Benchmark (surveying), Machine learning, Domain (mathematical analysis), Test data, Consistency (knowledge bases), Adversarial system, Domain adaptation, Adaptation (eye), Reliability (semiconductor), Pattern recognition (psychology), Optics, Classifier (UML), Programming language, Power (physics), Mathematics, Geodesy, Geology, Mathematical analysis, Physics, Quantum mechanics, Geography, SeismologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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