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Kernel flexible and...
Kernel flexible and displaceable convex hull based tensor machine for gearbox fault intelligent diagnosis with multi-source signals
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- He, Zhiyi (author)
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
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- Shao, Haidong (author)
- Luleå tekniska universitet,Drift, underhåll och akustik,State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
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- Cheng, Junsheng (author)
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
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- Yang, Yu (author)
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
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- Xiang, Jiawei (author)
- College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China
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(creator_code:org_t)
- Elsevier, 2020
- 2020
- English.
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In: Measurement. - : Elsevier. - 0263-2241 .- 1873-412X. ; 163
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- The methods based on traditional pattern recognition and deep learning have been successfully applied in gearbox intelligent diagnosis. However, traditional pattern recognition methods cannot directly classify feature tensors of multi-source signals, and deep learning networks hardly handle the classification of small samples. Therefore, for the gearbox intelligent diagnosis with multi-source signals, a novel tensor classifier called kernel flexible and displaceable convex hull based tensor machine (KFDCH-TM) is proposed. In KFDCH-TM, the kernel flexible and displaceable convex hull of tensor samples in tensor feature space is defined firstly. Then, an optimal separating hyper-plane between two kernel flexible and displaceable convex hulls is constructed. Meanwhile, feature tensors extracted from multi-source signals through wavelet packet transform (WPT) are used to diagnose gearbox fault by KFDCH-TM. The results of two cases demonstrate that KFDCH-TM can effectively identify gearbox fault with multi-source signals and has better robustness.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Annan samhällsbyggnadsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Other Civil Engineering (hsv//eng)
Keyword
- Gearbox intelligent diagnosis
- feature tensor
- multi-source signals
- kernel flexible and displaceable convex hull
- tensor machine
- Drift och underhållsteknik
- Operation and Maintenance
Publication and Content Type
- ref (subject category)
- art (subject category)
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