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Intelligent fault d...
Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging
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- Li, Xin (författare)
- School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, PR China
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- Li, Yong (författare)
- School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, PR China
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- Yan, Ke (författare)
- Department of the Built Environment, College of Design and Engineering, National University of Singapore, 4 Architecture Drive, 117566, Singapore
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- Shao, Haidong (författare)
- College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, PR China
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- Lin, Janet (Jing) (författare)
- Mälardalens universitet,Luleå tekniska universitet,Drift, underhåll och akustik,School of Innovation, Design and Engineering, Mälardalen University, Eskilstuna 63220, Sweden,Innovation och produktrealisering
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(creator_code:org_t)
- Elsevier, 2023
- 2023
- Engelska.
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Ingår i: Reliability Engineering & System Safety. - : Elsevier. - 0951-8320 .- 1879-0836. ; 230
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Fault diagnosis is of great significance to ensure the reliability and safety of complex bevel gearbox systems. Most existing intelligent fault diagnosis approaches of bevel gearboxes are designed with vibration monitoring. However, the collected vibration data are vulnerable to noise pollution and machinery operating conditions. Besides, traditional fault diagnosis models highly rely on numerous labeled samples, and neglect the high cost of label annotation in real-world applications. Therefore, a novel fault diagnosis approach based on semi-supervised probability support matrix machine (SPSMM) and infrared imaging is proposed for bevel gearboxes in this paper, which has the following properties. Firstly, SPSMM classifies 2D matrix data directly without vectorization, thus fully utilizing the spatial information in infrared images. Secondly, a probability output strategy is designed for SPSMM to calculate the posterior class probability estimation of matrix inputs, and consequently enhance the diagnostic accuracy and robustness of the model. Thirdly, a semi-supervised learning (SSL) framework is proposed for SPSMM to carry out sample transfer from the unlabeled sample pool to the labeled sample pool, which can effectively alleviate the problem of insufficient labeled samples. The superiority of the proposed diagnosis approach is demonstrated with an infrared imaging dataset of a bevel gearbox.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Tillförlitlighets- och kvalitetsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Reliability and Maintenance (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering (hsv//eng)
Nyckelord
- Intelligent fault diagnosis
- Support matrix machine
- Probability output strategy
- Semi-supervised learning
- Infrared imaging
- Drift och underhållsteknik
- Operation and Maintenance
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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