@inproceedings{30af966aaf3341a2972c021d7698b09e,
title = "Research on Health Management Technology of Oil Production Machine Based on Vibration Signal",
abstract = "Using vibration signal to identify mechanical equipment fault has become one of the main methods of mechanical fault detection, which has the advantages of no damage to the internal structure, no need to disassemble the equipment, simple installation and so on. This method is widely used in various fields. This paper uses EMD to decompose the vibration signal, establishes AR model for feature analysis, uses SVD to get the singular value as the feature of the vibration signal, and uses BP and GA-BP to train the system. The results show that the accuracy of GA-BP prediction is more than 95%, which meets the requirements of the system. This method can quickly identify the wear degree of crankshaft by using vibration signal without dismantling the equipment, which provides great convenience for vehicle fault detection.",
keywords = "AR, Diesel engine, EMD, GA-BP, Mechanicalfaultdetection, SVD, Vibrationsignal",
author = "Yingshun Li and Yu Tian and Xiaojian Yi and Haiyang Liu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021 ; Conference date: 13-08-2021 Through 15-08-2021",
year = "2021",
doi = "10.1109/SDPC52933.2021.9563359",
language = "English",
series = "Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "94--99",
editor = "Xuyun Fu and Shengcai Deng and Diego Cabrera and Yongjian Zhang and Zhiqiang Pu",
booktitle = "Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021",
address = "United States",
}