Improvement and application of D-S evidence theory in multi-sensor fault diagnosis system

被引：3
|
作者：
Li Y. [1 ,2 ]
Xu Y. [2 ]
Chen G. [2 ]
Miao R. [1 ]
Yu J. [1 ]
机构：
[1] School of Information Science and Engineering, East China University of Science and Technology
[2] School of Electrical Engineering, Shanghai Dianji University
来源：
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2011年 / 41卷 / SUPPL. 1期
关键词：
Dempster-Shafer (D-S) evidence theory; Evidence average distance; Fault diagnosis; Fuzzy member function; Information fusion;
D O I：
10.3969/j.issn.1001-0505.2011.S1.022
中图分类号：
学科分类号：
摘要：
In order to solve the problem that it is inconsistent with the facts when the conventional evidence theory is used to deal with evidences of high conflict in the multi-sensor information fusion fault diagnosis system. This paper introduces the basic framework of the Dempster-Shafer (D-S) evidence theory and analyzes shortcomings of assigning high believe probability to fault source of small possibility in the course of dealing with fusion of evidences of high conflict using the D-S evidence theory. A new method based on the fuzzy membership function and the evidence average distance is proposed. The fuzzy membership function fully considers the influence of expert knowledge to basic probability assignments and the importance index to evidences assigned by the evidence average distance can lower believe possibility of conflict evidences and increase its uncertain possibility. Reallocate the basic probability assignments by combining the evidence sufficiency index and the importance index from the evidence average distance. The experimental results show that the new method can improve the reliability and accuracy of fault diagnosis results and enhance the performance of the system.
引用
收藏
页码：102 / 106
页数：4
相关论文
共 16 条
  • [1] Chen F., Huang S., Zhang Y., Et al., Methods and application of information fusion fault diagnosis in generator unites, Journal of Vibration, Measurement and Diagnosis, 25, 1, pp. 17-20, (2005)
  • [2] Kaftandjian V., Zhu Y., Dupus O., Et al., The combined use of the evidence theory and fuzzy logic for improving multimodal nondestructive testing systems, IEEE Trans on Instrumentation and Measurement, 54, 5, pp. 1968-1977, (2005)
  • [3] Rottensteiner F., Trinder J., Clode S., Using the D-S method for the fusion of LIDAR data and multi-spectral images for building detection information fusion, Proceedings of the 7th International Conference on Information Fusion, pp. 1396-1403, (2005)
  • [4] (2004)
  • [5] Fan X.F., Zuo M.J., Fault diagnosis of machines based on D-S evidence theory. Part 1: D-S evidence theory and its improvement, Pattern Recognition Letters, 27, 5, pp. 366-376, (2006)
  • [6] Dong Y., Han Y., Liu J., Improvement of D-S evidence theory in multi-sensor target identification system, Journal of Projectiles, Rockets, Missiles and Guidance, 29, 4, pp. 221-221, (2009)
  • [7] Liu Z., Cheng Y., Pan Q., Et al., Combination of weighted belief functions based on evidence distance and conflict belief, Control Theory and Applications, 26, 12, pp. 1439-1442, (2009)
  • [8] Beynon M., Cosker D., Marshall D., An expert system for multicriteria decision making using Dempster-Shafer theory, Expert Syst Appl, 20, 4, pp. 357-367, (2001)
  • [9] Parikh C.R., Pont M.J., Jones N.B., Application of Dempster-Shafer theory in condition monitoring applications: A case study, Pattern Recognition Lett, 22, 6-7, pp. 777-785, (2001)
  • [10] Huang H.Z., Fuzzy multi-objective optimization decision-making of reliability of series system, Microelectron Reliab, 37, 3, pp. 447-449, (1997)
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