Open Access
MATEC Web of Conferences
Volume 22, 2015
International Conference on Engineering Technology and Application (ICETA 2015)
Article Number 03007
Number of page(s) 6
Section Mechanic and Control Engineering
Published online 09 July 2015
  1. Wang Xin. 2014. The Fault Diagnosis Technology of Mechanical Equipment and Application. Beijing: China Coal Industry Publishing House. [Google Scholar]
  2. Sitao Wu & Tommy W.S. Chow. 2004. Induction machine fault detection using SOM-based RBF neural networks. IEEE Transactions on Industrial Electronics. [Google Scholar]
  3. Rolf Isermann. 2005. Model-based fault-detection and diagnosis-status and applications. Annual Reviews in Control. [Google Scholar]
  4. Timothy L. Skvarenina. 2002. The Power Electronics Handbook. CRC Press LLC. [Google Scholar]
  5. Wang Quanxian. 2013. The Fault Diagnosis Technology of Mechanical Equipment. Wuhan: Huazhong University of Science and Technology Press. [Google Scholar]
  6. Wang Yonghua, Chen Huagang. 2014. Electrical Equipment Fault Diagnosis Technology. Beijing: Chinese Power Press. [Google Scholar]
  7. Morpurgo R, Mussi S. 2001. I-DSS: An intelligent diagnostic support system. Expert Systems. [Google Scholar]
  8. Jun Liao, Meng Joo Er, Lin Jianya. 2000. Application of a system for the automatic generation of fuzzy neural networks. Engineering Application of Artificial Intelligence. [Google Scholar]
  9. Yang Jun, Feng Zhengsheng, Zhang Xien, et al. 2001. Study on missile intelligent fault diagnosis system based on fuzzy NN expert system. Journal of Systems Engineering and Electronics. [Google Scholar]
  10. Chrissanthi Angeli. 2001. Derek Atherton. A model-based method for an on-line diagnostic knowledge-based system. Expert Systems. [Google Scholar]
  11. Smola A, Scholkopf B. 1998. A tutorial on support vectoregression. Neuro COLT: Technical Report. [Google Scholar]
  12. Larsson, J.E. 2000. Avoiding human error. Proceedings of the International Conference on Control and Instrumentation in Nuclear Installations, Bristol, England. [Google Scholar]
  13. Achmad Widodo, Bo Suk Yang, Tian Han. 2006. Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Systems with Applications. [Google Scholar]
  14. Escobet T, Feroldi D, Delira S. 2008. Model based fault diagnosis in PEM fuel cell systems. Journal of Power Sources. [Google Scholar]
  15. Yang Fuqiang. 2012. Application of non-destructive testing technology in coal mine safety inspection in mechanical equipment. Mining Machinery. [Google Scholar]
  16. Feng Tengfei, Yang Jingsheng. 2012. Application of fault analysis in equipment management. Science and Technology Enterprises. [Google Scholar]
  17. Yang Chao, Li Yitao. 2011. Status and development of intelligent diagnosis technology of mechanical equipment fault. Journal of East China Jiaotong University. [Google Scholar]
  18. Michael G P. 2008. Prognostics and Health Management of Electronics. New Jersey: John Wiley & Sons. Inc. Hoboken. [Google Scholar]
  19. Fan J Q, Yao Q W. 2003. Nonlinear Time Series: Non-parametric and Parametric Methods. USA: Springer. [Google Scholar]
  20. Zhao Jiong, Zhou Qicai, Xiong Xiaolei. 2014. Technology of Remote Fault Diagnosis and Maintenance. Machinery Industry Press. [Google Scholar]
  21. Xia Hong. 2010. The Fault Diagnosis Technology of Equipment. Harbin Institute of Technology Press. [Google Scholar]

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