J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2015 , Vol. 20 >Issue 4: 427 - 436

DOI: https://doi.org/10.1007/s12204-015-1644-5

Dynamic Fault Diagnosis Using the Improved Linear Evidence Updating Strategy

Expand
  • (1. College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; 2. Institute of Systems Science and Control Engineering, Hangzhou Dianzi University, Hangzhou 310018, China)

Online published: 2015-08-05

Fold

Abstract

The majority of the existing fault diagnosis methods using Dempster-Shafer (DS) evidence theory (DST) all provide the “static” fused results by combining several pieces of diagnosis evidence, which only reflect the current running status of monitored equipment. This paper presents the dynamic diagnosis strategy by using recursively the improved linear evidence updating rule. Its updated result can synthesize the diagnosis evidence collected at historical, current and future time steps by dynamically adjusting the proposed smoothing linear combination weights. The diagnosis examples of machine rotor show that the proposed method can provide more reliable and accurate results than the diagnosis methods based on the classical updating strategies.

Key words： dynamic fault diagnosis; information fusion; Dempster-Shafer evidence theory (DST); evidence updating

Cite this article

SHANG Qun-li1 (尚群立), ZHANG Zhen2 (张 镇), XU Xiao-bin2* (徐晓滨) . Dynamic Fault Diagnosis Using the Improved Linear Evidence Updating Strategy[J]. Journal of Shanghai Jiaotong University(Science), 2015 , 20(4) : 427 -436 . DOI: 10.1007/s12204-015-1644-5

References

[1] Wen Cheng-lin, Xu Xiao-bin. Multi-source uncertain information fusion theory and its application in fault diagnosis and reliability evaluation [M]. Beijing: Science Press, 2012: 180-192 (in Chinese).
[2] Shafer G. A mathematical theory of evidence [M].Princeton, USA: Princeton University Press, 1976: 25-28.
[3] Xu Xiao-bin, Wen Cheng-lin, Wang Ying-chang. Information fusion algorithm of fault diagnosis based on random set metrics of fuzzy fault features [J]. Journal of Electronics and Information Technology, 2009,31(7): 1635-1640 (in Chinese).
[4] Oukhello L, Debioless A, Denoeux T. Fault diagnosis in railway track circuits using Dempster-Shafer classifier [J]. Engineering Applications of Artificial Intelligence,2010, 23(1): 117-128.
[5] Fan Xian-feng, Zuo Ming-jin. Fault diagnosis of machines based on DS evidence theory. Part 1. D-S evidence theory and its improvement [J]. Pattern Recognition Letters, 2006, 27: 377-385.
[6] Shafer G. Jeffrey’s rule of conditioning [J]. Philosophy of Science, 1981, 48: 337-362.
[7] Smets P. The transferable belief model and random sets [J]. International Journal of Intelligent System,1992, 7(1): 37-46.
[8] Dubios D, Prade H. Updating with belief function,ordinal conditional functions and possibility measures[J]. Uncertainty in Artificial Intelligence, 1991, 6: 307-315.
[9] Kulasekere E C, Premaratne K, Dewasurendra D A, et al. Conditioning and updating evidence [J]. International Journal of Approximate Reasoning, 2004,36(1): 75-108.
[10] Smets P, Kennes R. The transferable belief model[J]. International Journal of Artificial Intelligence,1994, 66: 191-234.
[11] Fagin R, Halpern J Y. A new approach to updating beliefs [C]//Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence. Cambridge, UK:Elsevier, 1991: 317-325.
[12] Wang Yu-cheng, Xu Xiao-bin, Lu Yan-wen, et al. The fusion method of conflicting evidence based on the cosine similarity of evidence vectors [C]//The 8th IEEE International Conference on Control and Automation.Xiamen, China: IEEE, 2010: 1279-1283.
[13] Deng Yong, Shi Wen-kang, Liu Qi. Combining belief function based on distance function [J]. Decision Support Systems, 2004, 38(3): 486-493.
Options
Outlines

/

〈 〉