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Unsupervised domain...
Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds
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- Cao, Hongru (författare)
- College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
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- Shao, Haidong (författare)
- Luleå tekniska universitet,Drift, underhåll och akustik,College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
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- Zhong, Xiang (författare)
- College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
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- Deng, Qianwang (författare)
- College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
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- Yang, Xingkai (författare)
- Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
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- Xuan, Jianping (författare)
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074 China
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(creator_code:org_t)
- Elsevier, 2022
- 2022
- Engelska.
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Ingår i: Journal of manufacturing systems. - : Elsevier. - 0278-6125 .- 1878-6642. ; 62, s. 186-198
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The existing deep transfer learning-based intelligent fault diagnosis studies for machinery mainly consider steady speed scenarios, and there exists a problem of low diagnosis efficiency. In order to overcome these limitations, an unsupervised domain-share convolutional neural network (CNN) is proposed for efficient fault transfer diagnosis of machines from steady speeds to time-varying speeds. First, a Cauchy kernel-induced maximum mean discrepancy based on unbiased estimation is developed for improving the efficiency and robustness of feature adaptation. Secondly, an unsupervised domain-share CNN is constructed to simultaneously extract the domain-invariant features from the source domain and the target domain. Finally, adjustable and segmented balance factors are designed to flexibly weigh the distribution-adaptation loss and cross-entropy loss to improve diagnosis accuracy and transferability. The proposed method analyzes raw vibration signals collected from bearings and gears under different rotating speeds. Results of case studies show that the proposed method can achieve higher diagnosis accuracy, faster convergence, and better robustness than the reported methods, which demonstrates its potential applications in machine fault transfer diagnosis from a steady speed condition to a time-varying speed condition.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Unsupervised domain-share CNN
- Fault transfer diagnosis
- Time-varying speeds
- Cauchy kernel-induced maximum mean difference
- Adjustable and segmented factors
- Drift och underhållsteknik
- Operation and Maintenance
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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