Table of Contents Author Guidelines Submit a Manuscript
Corrigendum

A corrigendum for this article has been published. To view the corrigendum, please click here.

Mathematical Problems in Engineering
Volume 2015, Article ID 563954, 9 pages
http://dx.doi.org/10.1155/2015/563954
Research Article

A Diagnosis Method for Rotation Machinery Faults Based on Dimensionless Indexes Combined with -Nearest Neighbor Algorithm

1Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Maoming 525000, China
2School of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming 525000, China
3School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Received 30 October 2014; Revised 3 February 2015; Accepted 3 February 2015

Academic Editor: Gang Li

Copyright © 2015 Jianbin Xiong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Q. H. Zhang, Fault Diagnosis in Unit Based on Artificial Immune Detectors System, China Petrochemical Press, 2008.
  2. A. S. Qing, Q. H. Zhang, T. Y. Li, and Q. Hu, “The application of a compound dimensionless parameter for fault classifying of rotating machinery,” Modern Manufacturing Engineering, no. 4, pp. 10–14, 2013. View at Google Scholar
  3. Q. H. Zhang and Y. Z. Fu, “Research of adaptive immune network intrusion detection model,” International Journal of Systems, Control and Communications, vol. 3, no. 3, pp. 280–286, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. X.-S. Si, C.-H. Hu, J.-B. Yang, and Q. Zhang, “On the dynamic evidential reasoning algorithm for fault prediction,” Expert Systems with Applications, vol. 38, no. 5, pp. 5061–5080, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. X. S. Si, C. H. Hu, and Z. J. Zhou, “Fault prediction model based on evidential reasoning approach,” Science in China, Series F: Information Sciences, vol. 53, no. 10, pp. 2032–2046, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. L. Zhang, J.-W. Liu, R.-C. Wang, and H.-Y. Wang, “Trust evaluation model based on improved D-S evidence theory,” Journal on Communications, vol. 34, no. 7, pp. 167–173, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. H.-S. Feng, X.-B. Xu, and C.-L. Wen, “A new fusion method of conflicting interval evidence based on the similarity measure of evidence,” Journal of Electronics and Information Technology, vol. 34, no. 4, pp. 851–857, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. H.-W. Guo, W.-K. Shi, Q.-K. Liu, and Y. Deng, “New combination rule of evidence,” Journal of Shanghai Jiaotong University, vol. 40, no. 11, pp. 1895–1902, 2006. View at Google Scholar · View at Scopus
  9. R. R. Yager, “On the Dempster-Shafer framework and new combination rules,” Information Sciences, vol. 41, no. 2, pp. 93–137, 1987. View at Publisher · View at Google Scholar · View at MathSciNet
  10. D. Dubois and H. Prade, “Representation and combination of uncertainty with belief functions and possibility measures,” Computational Intelligence, vol. 4, no. 3, pp. 244–264, 1988. View at Publisher · View at Google Scholar
  11. B. C. Li, B. Wang, J. Wei, C. B. Qian, and Y. Q. Huang, “Efficient combination rule of evidence theory,” Journal of Data Acquisition and Processing, vol. 17, no. 1, pp. 33–36, 2002. View at Google Scholar · View at Scopus
  12. D. Yong, S. WenKang, Z. ZhenFu, and L. Qi, “Combining belief functions based on distance of evidence,” Decision Support Systems, vol. 38, no. 3, pp. 489–493, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. W. Liu, “Analyzing the degree of conflict among belief functions,” Artificial Intelligence, vol. 170, no. 11, pp. 909–924, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. J. B. Xiong, Q. H. Zhang, G. X. Sun, Z. P. Peng, and Q. Liang, “Fusion of the dimensionless parameters and filtering methods in rotating machinery fault diagnosis,” Journal of Networks, vol. 9, no. 5, pp. 1201–1207, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Wang, Study on text categorization based on decision tree and K nearest neighbors [Ph.D. thesis], Tientsin University, 2006.
  16. Q. Ye, X.-P. Wu, and Y.-X. Song, “Evidence combination method based on the weight coefficients and the confliction probability distribution,” Systems Engineering and Electronics, vol. 28, no. 7, pp. 1014–1081, 2006. View at Google Scholar · View at Scopus
  17. E. Lefevre, O. Colot, and P. Vannoorenberghe, “Belief function combination and conflict management,” Information Fusion, vol. 3, no. 2, pp. 149–162, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. B. He and H. L. Hu, “Multi-level DS evidence combination strategy,” Computer Engineering and Applications, vol. 10, pp. 87–90, 2004. View at Google Scholar
  19. Q. Sun, X. Ye, and W. K. Gu, “A new combination rules of evidence theory,” Acta Electronica Sinica, vol. 28, no. 8, pp. 117–119, 2000. View at Google Scholar
  20. J. B. Xiong, Intelligence data fusion and its applications in ship dynamic positioning, Guangdong university of technology [Ph.D. thesis], Guangdong University of Technology, 2012.