@inproceedings{e938a1f367af4d63869c3f163bc0351f,
title = "Research on On-line Monitoring Technology of Oil Wear Particles Based on Improved Otsu Algorithm",
abstract = "Aiming at the catastrophic consequences of large mechanical equipment due to problems with the lubrication system, a study based on an improved Otsu algorithm is proposed. This article elaborates the significance of online monitoring of abrasive particles in oil and the methods to achieve online monitoring. On the premise of the research of traditional Otsu algorithm, the existing algorithm is analyzed and improved. The experimental part is based on MATLAB simulation link to model and analyze oil abrasive grains. The experimental results show that the designed oil abrasive grain detection algorithm can meet the requirements of stable and effective detection of abrasive grain size and quantity and can distinguish quantitatively. 100um abrasive particles, and the detection rate is not less than 80\%, and the performance is good, which meets the design requirements.",
keywords = "Otsu, Threshold segmentation, algorithm, style",
author = "Yingshun Li and Xiangguang Meng and Xiaojian Yi and Jianxin He and Zhe He",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 4th International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2020 ; Conference date: 05-08-2020 Through 07-08-2020",
year = "2020",
month = aug,
day = "5",
doi = "10.1109/SDPC49476.2020.9353143",
language = "English",
series = "Proceedings of 2020 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "22--25",
editor = "Yong Qin and Zuo, \{Ming J.\} and Xiaojian Yi and Limin Jia and Dejan Gjorgjevikj",
booktitle = "Proceedings of 2020 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2020",
address = "United States",
}