@inproceedings{0284a7248d834bd5a140dc58cf4bbdbb,
title = "Research on rotor condition monitoring based on D-S evidence theory",
abstract = "Aiming at the problem of continuous monitoring of rotating machinery, a new method based on D-S evidence theory is proposed for rotor condition monitoring. The discriminant vectors can be constructed by calculating the time domain parameters and frequency domain parameters from online monitoring data. Then the Euclidean distance between the discriminant vector and the standard vectors is calculated to acquire the probability of each rotor operating state. The multi-channel information is integrated to acquire the results of time domain and frequency domain by D-S evidence theory. Finally the final recognition result is obtained by fusing the time domain and frequency domain results. Experimental results show that the proposed method can improve the accuracy of rotor state identification and it has a good ability to distinguish the typical rotor operating states.",
keywords = "Condition monitoring, D-S evidence theory, Frequency domain parameters, Rotor, Time domain parameters",
author = "Yuanchao Chen and Guangrui Wen and Xiaoni Dong and Zhifen Zhang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016 ; Conference date: 19-08-2016 Through 22-08-2016",
year = "2016",
month = oct,
day = "21",
doi = "10.1109/URAI.2016.7733993",
language = "英语",
series = "2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "848--853",
booktitle = "2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016",
}