@inproceedings{d54615e4801b488591177388e30b8002,
title = "Nonstationary signal de-noising method of slow-speed large-size slewing bearing using robust local mean decomposition",
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.",
keywords = "Kernel principal component analysis, Robust local mean decomposition, Signal de-noising, Slewing bearing",
author = "Yubin Pan and Hua Wang and Jie Chen and Rongjing Hong",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; 2021 International Conference on Intelligent Equipment and Special Robots, ICIESR 2021 ; Conference date: 29-10-2021 Through 31-10-2021",
year = "2021",
doi = "10.1117/12.2625248",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Qiang Zhang and Zhong You",
booktitle = "International Conference on Intelligent Equipment and Special Robots, ICIESR 2021",
address = "美国",
}