Intelligent Fault Diagnosis Method Based on Reliability Analysis

Article Preview

Abstract:

This paper presents a novel fault diagnosis method which overcomes the shortcomings of traditional approaches. It uses dynamic fault tree model for reliability analysis. All minimal cut sequences are generated via a new modular method, while the diagnostic importance factor (DIF) of components and minimal cut sequences are calculated using discrete-time Bayesian network. Also, we introduce the cost of diagnostic importance factor (CDIF) for components to evaluate the influences of the test costs, and combine it with minimal cut sequences’ DIF to determine the order of the system diagnosis. Finally, an example is given to demonstrate the effectiveness of this method.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

487-491

Citation:

Online since:

October 2010

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2010 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Zhou Dong-Hua, Hu Yan-Yan: Fault Diagnosis Techniques for Dynamic Systems, Acta Automatica Sinica, Vol. 35, No. 6, (2009), pp.748-757.

DOI: 10.3724/sp.j.1004.2009.00748

Google Scholar

[2] Moret, BM: Decision trees and diagrams, Computing Surveys, Vol. 14, No. 4, (1982), pp.593-623.

Google Scholar

[3] ASSAF T, DUGAN J B: Design for diagnosis using a diagnostic evaluation measure, IEEE Instrumentation and Measurement Magazine, Vol. 4, (2006), pp.37-43.

DOI: 10.1109/mim.2006.1664040

Google Scholar

[4] Zhang Qi, Liao Jie, Wu Jian-jun, Liu Yu: Design of diagnostic expert system for electronic system of general equipment based on FTA, ACTA ARMAMENTARII, Vol. 29, No. 2, (2008), pp.175-177.

Google Scholar

[5] Ni Shaoxu, Zhang Yufang, Yi Hong: Intelligent Fault Diagnosis Method Based on Fault Tree, JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY, Vol. 42, No. 8, (2008), pp.1372-1386.

Google Scholar

[6] Tao Yongjian, Dong Decun, Ren Peng: An improved method for system fault diagnosis using fault tree analysis, JOURNAL OF HARBIN INSTITUTE OF TECHNOLOGY, Vol. 42, No. 1, (2010), pp.143-147.

Google Scholar

[7] Dong Liu, Weiyan Xing, Chunyuan Zhang, Rui Li and Haiyan Li: Cut Sequence Set Generation for Fault Tree Analysis, Lecture Notes in Computer Science, Vol. 4523 (2007), pp.592-603.

DOI: 10.1007/978-3-540-72685-2_55

Google Scholar

[8] Dugan, Joanne Bechta, Sullivan, Kevin J., Coppit, David: Developing a low-cost high-quality software tool for dynamic fault tree analysis, IEEE Transactions on Reliability, Vol. 49, No. 1, (2000), pp.49-59.

DOI: 10.1109/24.855536

Google Scholar

[9] Assaf, Tariq, Dugan, Joanne Bechta: Build better diagnostic decision trees, IEEE Instrumentation and Measurement Magazine, Vol. 8, (2005), pp.48-53.

DOI: 10.1109/mim.2005.1502449

Google Scholar

[10] Boudali, H, Dugan, J.B.: A discrete-time Bayesian network reliability modeling and analysis framework, Reliability Engineering and System Safety, Vol. 87, No. 3, (2005), pp.337-349.

DOI: 10.1016/j.ress.2004.06.004

Google Scholar

[11] Tong D.W., Jolly C.H., and Zalondek, K.C.: Diagnostic tree design with model based reasoning, AUTOTESTCON '89, IEEE Automatic Testing Conference, (1989), pp.161-167.

DOI: 10.1109/autest.1989.81115

Google Scholar

[12] Koutsoukos X, Zha F., Haussecker H., and P. Cheung: Fault modeling for monitoring and diagnosis of sensor-rich hybrid system, Proceedings of the 40th IEEE conference on decision and control, Vol. 1, (2001), pp.793-801.

DOI: 10.1109/cdc.2001.980203

Google Scholar