@inproceedings{b095bb2041a64b5ba1d284660a2c13fb,
title = "An engine oil analysis method based on kernel density estimation and three-lines values method",
abstract = "In order to solve the problem of small sample data and difficulty to judge the working state of machinery machine in oil analysis, an oil analysis method based on kernel density estimation and three-line values method is proposed. First, kernel density estimation is used to solve the unbiased estimation of the oil sample data, and the statistical average and standard deviation of the samples are obtained by the probability density function. Then, combined with the three-line values method, the normal line, warning line and danger line are generated to judge the working state of the engine, and the results are obtained. Finally, the proposed method is used to analyze and judge the oil data of an engine, based on the given criteria. The results imply that the method is effective.",
keywords = "kernel density estimation, oil analysis, three-line values method, wear loss",
author = "Zhencong Lu and Mengzhou Liu and Yong Qin and Ge Xin and Yuze Wang and Shunjie Zhang and Xiaoqing Cheng and Xiaojian Yi",
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/SDPC49476.2020.9353129",
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 = "409--414",
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",
}