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A multi-sensor sign...
A multi-sensor signals denoising framework for tool state monitoring based on UKF-CycleGAN
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- Wei, Xudong (författare)
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
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- Liu, Xianli (författare)
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
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- Yue, Caixu (författare)
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
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- Wang, Lihui (författare)
- KTH,Industriella produktionssystem
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- Liang, Steven Y. (författare)
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta 30332, USA
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- Qin, Yiyuan (författare)
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
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(creator_code:org_t)
- Elsevier BV, 2023
- 2023
- Engelska.
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Ingår i: Mechanical systems and signal processing. - : Elsevier BV. - 0888-3270 .- 1096-1216. ; 200
- 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 denoising of mechanical system is always an indispensable process in sensor signal analysis. It directly affects the result of subsequent tool state monitoring and identification. Therefore, a denoising framework is proposed to solve this problem. Bayesian nonparametric estimation instead of the Gaussian fitting distribution of CycleGAN can ensure the quality of denoising data to the greatest extent. The experiment of milling 42CrMo steel was carried out, and the proposed method was verified. Compared with the wavelet packet threshold, the signal-to-noise ratio (SNR) obtained by the propose model is increased by 4.71 dB on average, and RMSE ranges from 0.0210 to 0.0642. UKF-CycleGAN model has better denoising effect than other methods. The model proposed in this paper improves the accuracy of tool wear identification. At the same time, the process of selecting the parameters for denoising model by manual experience can be reduced. This provides the possibility for online denoising of sensor signals in milling process, which has certain guiding significance for tool state monitoring in machinery industry.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Denoising framework
- Face milling
- Multi-sensor signals
- Nonparametric estimation
- Tool state monitoring
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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