Research on Rotor Condition Monitoring based on D-S Evidence Theory

被引:0
作者
Chen, Yuanchao [1 ,2 ]
Wen, Guangrui [1 ,2 ,3 ]
Dong, Xiaoni [1 ,2 ]
Zhang, Zhifen [1 ,2 ]
机构
[1] Educ Minist Modern Design & Rotor Bearing Syst, Key Lab, Xian, Peoples R China
[2] Xi An Jiao Tong Univ, Res Inst Diag & Cybernet, Xian 710049, Peoples R China
[3] Xinjiang Univ, Sch Mech Engn, Urumqi 830047, Peoples R China
来源
2016 13TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI) | 2016年
关键词
Rotor; Condition monitoring; Time domain parameters; Frequency domain parameters; D-S evidence theory;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:848 / 853
页数:6
相关论文
共 15 条
  • [1] [Anonymous], 2006, J MECH DES
  • [2] [卜乐平 Bu Leping], 2011, [振动、测试与诊断, Journal of Vibration, Measurement and Diagnosis], V31, P23
  • [3] Hou Jinghong, 2004, CHINESE J MECH ENG, V40
  • [4] Huang Wen-tao, 2003, Proceedings of the CSEE, V23, P150
  • [5] MARTIN N, 2007, OR INSIGHT, V49, P459, DOI DOI 10.1784/INSI.2007.49.8.459
  • [6] Qian J.G., 2006, COAL MINE MACHINERY, V27, P192
  • [7] Sun Ruiguo, 1992, MECH CONDITION MONIT
  • [8] Tang Gui-ji, 2014, Journal of Vibration Engineering, V27, P118
  • [9] WAN Shuting, 2010, MECH ENG AUTOMATION, V3, P109
  • [10] Applications of fault diagnosis in rotating machinery by using time series analysis with neural network
    Wang, Chun-Chieh
    Kang, Yuan
    Shen, Ping-Chen
    Chang, Yeon-Pun
    Chung, Yu-Liang
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (02) : 1696 - 1702