@inproceedings{cd961d41411f4973b4f36e4ea0d8bd49,
title = "A health self-sensing framework for electromechanical equipment using encoder signal",
abstract = "Health sensing plays a pivotal role to ensure the reliability of electromechanical equipment. However, conventional health sensing methods rely too heavily on additional sensors, while requirements of installation and wiring for external sensors limit their wide application in the era of industry 4.0. To this end, a health self-sensing approach for electromechanical equipment is proposed. Firstly, the health information is captured via rotary encoders that are more widely applied in electromechanical equipment. To separate the speed jitter caused by failure, a Gini-guided Robust principal component analysis (RPCA) method is proposed for further processing to realize self-sensing of equipment. Finally, the effectiveness of presented framework is verified by simulation and experiment.",
keywords = "electromechanical equipment, encoder signal, health monitoring, self-sensing",
author = "Shudong Ou and Sen Li and Changqing Wu and Mourui Luo and Ming Zhao",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2022 ; Conference date: 05-08-2022 Through 07-08-2022",
year = "2022",
doi = "10.1109/SDPC55702.2022.9915844",
language = "英语",
series = "Proceedings of 2022 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "67--70",
editor = "Qibing Yu and Diego Cabrera and Jiufei Luo and Zhiqiang Pu",
booktitle = "Proceedings of 2022 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2022",
}