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12 December 2021 Nonstationary signal de-noising method of slow-speed large-size slewing bearing using robust local mean decomposition
Yubin Pan, Hua Wang, Jie Chen, Rongjing Hong
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Proceedings Volume 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021); 121272K (2021) https://doi.org/10.1117/12.2625248
Event: International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 2021, Qingdao, China
Abstract
As a key rotary connection component of construction machinery, the operation performance of slewing bearing has an impact on the stability of engineering construction. Condition monitoring for slewing bearing is essential to their high availability and profitable operation. However, the characteristics of slow-speed large-size slewing bearing make the weak vibration signal corrupted with noise. Therefore, effective signal de-noising for preprocessing technique is difficult but crucial. To solve this problem, a novel signal de-nosing method using robust local mean decomposition is proposed with a product function selection strategy based on kernel principal component analysis. The effectiveness is validated by using simulated as well as experimental vibration signals obtained through a slewing bearing highly accelerated life test. The results illustrate that proposed method can perform effective signal de-noising of slewing bearing compared with other conventional method.
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Yubin Pan, Hua Wang, Jie Chen, and Rongjing Hong "Nonstationary signal de-noising method of slow-speed large-size slewing bearing using robust local mean decomposition", Proc. SPIE 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 121272K (12 December 2021); https://doi.org/10.1117/12.2625248
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KEYWORDS
Interference (communication)
Principal component analysis
Signal processing
Accelerated life testing
Signal analysis
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