An evidential sensor fusion method in fault diagnosis

Advances in Mechanical Engineering

Volumn 8, Issue 3, 2016, Pages 1-7

  Jiang, Wen ^a   Wei, Boya ^a   Xie, Chunhe ^a   Zhou, Deyun ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

belief entropy; conflict management; Dempster Shafer evidence theory; Deng entropy; fault diagnosis; sensor fusion

Indexed keywords

EID: 84962376160     PISSN: 16878132     EISSN: 16878140     Source Type: Journal    
DOI: 10.1177/1687814016641820     Document Type: Article

References (44)

1. Paté-Cornell ME. Organizational aspects of engineering system safety: the case of offshore platforms. Science 1990; 250: 1210-1217.
2. Chen C, Brown D, Sconyers C, et al. An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems. Expert Syst Appl 2012; 39: 9031-9040.
3. Brito AJ and De Almeida AT. Multi-attribute risk assessment for risk ranking of natural gas pipelines. Reliab Eng Syst Safe 2009; 94: 187-198.
4. Zhou H, Jing X, Wang L, et al. Research on high speed cutting parameter optimization and fault diagnosis technology. Adv Mech Eng 2014; 6: Article ID 281216.
5. Oukhellou L, Debiolles A, Denoeux T, et al. Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion. Eng Appl Artif Intel 2010; 23: 117-128.
6. Liu Q, Chen F, Zhou Z, et al. Fault diagnosis of rolling bearing based on wavelet package transform and ensemble empirical mode decomposition. Adv Mech Eng 2013; 5: Article ID 792584.
7. Klir GJ and Folger TA. Fuzzy sets, uncertainty, and information. J Oper Res Soc 1990; 41: 233-250.
8. Deng Y, Mahadevan S and Zhou D. Vulnerability assessment of physical protection systems: a bio-inspired approach. Int J Unconv Comput 2015; 11: 227-243.
9. Fan X and Zuo MJ. Fault diagnosis of machines based on D-S evidence theory. Part 1: D-S evidence theory and its improvement. Pattern Recogn Lett 2006; 27: 366-376.
10. Blanke M, Kinnaert M, Lunze DIJ, et al. Diagnosis and fault-tolerant control. Technometrics 2012; 49: 493-494.
11. Cuzzolin F. A geometric approach to the theory of evidence. IEEE T Syst Man Cy C 2007; 38: 522-534.
12. Schubert J. Conflict management in Dempster-Shafer theory using the degree of falsity. Int J Approx Reason 2011; 52: 449-460.
13. Rao BSY, Durrant-Whyte HF and Sheen JA. A fully decentralized multi-sensor system for tracking and surveillance. Int J Robot Res 1993; 12: 20-44.
14. Basir O and Yuan X. Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory. Inform Fusion 2007; 8: 379-386.
15. Muller AC and Narayanan S. Cognitively-engineered multi-sensor image fusion for military applications. Inform Fusion 2009; 10: 137-149.
16. Feller W. An introduction to probability theory and its applications. Technometrics 1973; 9: 420-421.
17. Bernardo JM and Smith AFM. Bayesian theory. Meas Sci Technol 2001; 15: 13-23.
18. Gilmore CJ. An introduction to probability theory. Nato Asi 1998; 7: 33-44.
19. Jiang W, Yang Y, Luo Y, et al. Determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers. Int J Comput Commun 2015; 10: 333-347.
20. Shafer G. A mathematical theory of evidence. Math Theor Evid 1976; 20: 242.
21. Dempster AP. Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 1966; 38: 57-72.
22. Simon C, Weber P and Evsukoff A. Bayesian networks inference algorithm to implement Dempster-Shafer theory in reliability analysis. Reliab Eng Syst Safe 2008; 93: 950-963.
23. Su X, Mahadevan S, Xu P, et al. Dependence assessment in human reliability analysis using evidence theory and AHP. Risk Anal 2015; 35: 1296-1316.
24. Deng Y. A threat assessment model under uncertain environment. Math Probl Eng 2015; 2015: Article ID 878024 (12 pp.).
25. Cui H, Peng L, Song H, et al. Dempster-Shafer multifeature fusion for pedestrian detection. Adv Mech Eng 2015; 7: Article ID 454058.
26. Yang BS and Kim KJ. Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Inform Fusion 2006; 20: 403-420.
27. Zadeh LA. A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. Ai Magazine 1986; 7: 85-90.
28. Yager RR. On the Dempster-Shafer framework and new combination rules. Inform Sci Int J 1987; 41: 93-137.
29. Dubois D and Prade H. Representation and combination of uncertainty with belief functions and possibility measures. Comput Intell 2007; 4: 244-264.
30. Smets P. The combination of evidence in the transferable belief model. IEEE T Pattern Anal 1990; 12: 447-458.
31. Smets P. Belief functions - the disjunctive rule of combination and the generalized Bayesian theorem. Int J Approx Reason 1993; 9: 1-35.
32. Murphy CK. Combining belief functions when evidence conflicts. Decis Support Syst 2000; 29: 1-9.
33. Deng Y. Generalized evidence theory. Appl Intell 2015; 43: 530-543.
34. Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948; 27: 3-55.
35. Lin J. Divergence measures based on the Shannon entropy. IEEE T Inform Theory 1991; 37: 145-151.
36. Deng Y. Deng entropy: a generalized Shannon entropy to measure uncertainty. 2015, http://vixra.org/abs/1502.0222
37. Beynon M, Cosker D and Marshall D. An expert system for multi-criteria decision making using Dempster-Shafer theory. Expert Syst Appl 2001; 20: 357-367.
38. Zadeh LA. Fuzzy sets. Inform Contr 1965; 8: 338-353.
39. Lefevre E, Colot O and Vannoorenberghe P. Belief function combination and conflict management. Inform Fusion 2002; 3: 149-162.
40. Inagaki T. Interdependence between safety-control policy and multiple-sensor schemes via Dempster-Shafer theory. IEEE T Reliab 1991; 40: 182-188.
41. Daniel M. Associativity in combination of belief functions; a derivation of minC combination. Soft Comput 2003; 7: 288-296.
42. Fixsen D and Mahler RPS. The modified Dempster-Shafer approach to classification. IEEE T Syst Man Cy A 1997; 27: 96-104.
43. Lin Y, Si X, Zhou R, et al. Application of improved grey correlation algorithm on radiation source recognition. J Commun 2010; 31: 166-171.
44. Lin Y and Si X. A new optimization recognition method for radiation source. J Astronaut 2011; 32: 420-427.