ABSTRACT The paper presents a universal fault diagnostic expert system frame work. The frame work is characterized by two basic features. The first includes a fault diagnostic strategy which utilizes the fault classification and checks knowledge about unit under test. The degree of accuracy to which faults are located is improved by using fault classification knowledge. The second characteristic is object oriented inference mechanism using message passing. Object orientation in inference mechanism improved inference efficiency. The developed framework demonstrates its effectiveness and superiority compared to earlier approaches using case studies.
Cite this paper
D. Kodavade and S. Apte, "A Universal Object Oriented Expert System Frame Work for Fault Diagnosis," International Journal of Intelligence Science, Vol. 2 No. 3, 2012, pp. 63-70. doi: 10.4236/ijis.2012.23009.
 R. Davis, “Reasoning from First Principles in Electronic Troubleshooting,” International Journal of Man Machine Studies, Vol. 19, No. 5, 1983, pp. 403-423.
C. Angeli, “Diagnostic Expert Systems: From Expert’s Knowledge to Real—Time Systems,” Advanced Knowledg Based Systems (Model, Applications & Search), Vol. 1, 2010, pp. 50-73.
D. N. Batanov and Z. Cheng, “An Object—Oriented Expert System for Fault Diagnosis in Ethylene Distillation Process,” Computer in Industry, Vol. 27, No. 3, 2000, pp. 237-249. doi:10.1016/0166-3615(95)00035-2
N. Yang, S. Cheng, Z. Xu, et al., “An Expert System for Vibration Fault Diagnosis of Large Steam Turbine Generator Set,” Proceedings of 3rd IEEE International Conference on Computer Research & Development, Shanghai, 11-13 March 2011, Vol. 2, pp. 217-221.
J. W. Coffey, A. J. Canas, et al., “Knowledge Modeling and the Creation of EI-Tech: A Performance Support and Training System for Electronic Technicians,” International Journal on Expert Systems with Applications, Vol. 25, No. 4, 2003, pp. 483-492.
J. Y. Qu and L. Y. Liang, “A Production Rule Based Expert System for Electronic Control Automatic Transmission Fault Diagnosis,” Proceedings of 2009 IEEE International Conference on Test & Automation, Kobe, 12-17 May 2009, pp.3724-3729.
I. Borlea and A. Buta, “DIASE—Expert System Fault Diagnosis for Timisoara 22 kv Substation,” Proceedings of Eurocon, Belgrade, 22-24 November 2005, pp. 251- 255.
J. J. Chen and X. X. Chen, “Research on Embedded Airborne Electronic Fault Diagnosis Expert System,” Proceedings of 2nd International Conference on Information Engineering & Computer Science (ICIECS), Wuhan, 25- 26 December 2010, pp. 1-5.
T. Han, B. Li and L. M. Xu, “A Universal Fault Diagnostic Expert System Based on Bayesian Network,” Proceedings of 2008 IEEE International Conference on Computer Science & Software Engineering, Wuhan, 12-14 December 2008, pp. 260-263.
S. Gebus and K. Leiviska, “Knowledge Acquisition for Decision Support Systems on an Electronic Assembly Line,” International Journal on Expert Systems with Applications, Elsevier, Vol. 36, No. 1, 2009, pp. 93-101.
C.-S. Liu, S.-J. Zhang and S.-S. Hu, “Adaptive Neural -Network—Based Fault Detection and Diagnosis Using Unmeasured States,” International Journal on IET Control Theory Applications, Vol. 2, No. 12. 2008, pp. 1066- 1076.
Y.-C. Liang, X.-Y. Sun, D.-H. Liu, et al., “Applications of Combinatorial Probabilistic Nural Network in Fault Diagnosis of Power Transformer,” Proceedings of 6th IEEE International Conference on Machine Learning and Cybernetics, Vol. 2, 2007, pp. 1115-1119.
D. Grzechca and J. Rutkowski, “Use of Neuro-Fuzzy System to Time Domain Electronic Circuit Fault Diagnosis,” ICSC Congress on Computational Intelligence Methods and Applications, 2005.
Y. H. Tan, Y. G. He, C. Cui and G. Y. Qiu, “A Novel Method for Analog Fault Diagnosis Based on Neural Networks and Genetic Algorithms,” IEEE Transaction on Instrumentation and Measurement, Vol. 57, No. 11, 2008, pp. 2631-2635.
R. Senjen, M. de Beler, C. Leckie, et al., “Hybrid Expert Systems for Monitoring and Fault Diagnosis,” Proceedings of 9th Conference on Artificial Intelligence for Applications, Orlando, 1-5 March 1993, pp. 235-241.
Q. M. Li, L. Zhu and Z. Xu, “Fuzzy Petri-Nets Based Fault Diagnosis for Mechanical—Electrical Equipment,” Proceedings of 2007 IEEE International Conference on Control and Automation, Guangzhou, 30 May-1 June 2007, pp. 2539-2543.
A. Ramfrez, et al., “Online Fault Diagnosis of Discrete Event Systems. A Petri Net-Based Approach,” IEEE Transaction on Automation Science and Engineering, Vol. 4, No. 1, 2007. pp. 31-39.
C. L. Zhou and Z. C. Jiang, “Fault Diagnosis of TV Transmitters Based on Fuzzy Petri Nets,” Proceedings of IMACS Multiconference on Computational Engineering in Systems Applications, October 2006, pp. 2003-2007.
Yan Qu, et al., “A Fuzzy Expert System Framework Using Object Oriented Technique,” Proceeding of 2008 IEEE Pacific—Asia Workshop on Computational Intelligence and Industrial Applications, Wuhan, 19-20 December 2008, Vol. 2, pp. 474-478.
M. Karakose, I. Aydin and E. Akin, “The Intelligent Fault Diagnosis Framework Based on Fuzzy Integral,” SPEEDAM 2010 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, Pisa, 14-16 June 2010, pp. 1634-1639.
X.-L. Liang, Y.-X. Zhao, et al., “Research on Applications of Fuzzy Fault Tree Analysis in the Electronics Equipment Fault Diagnosis,” IEEE System Engineering & Electronics, Singapore, 26-28 February 2010, pp. 65-67
D. B. Manner, “TROUBLE III: A Fault Diagnostic Expert System for Space Station Freedom’s Power System,” NASA Report.
P.-C. Hsu and S.-J. Wang, “Testing and Diagnosis of Board Interconnects in Microprocessor-Based Systems,” Proceedings of 5th Asian Test Symposium, Hsinchu, 20-22 November 1996, pp. 56-61.
B. Van Ngo and P. Law, et al., “Use of JTAG Boundary —Scan for Testing Electronic Circuit Boards and Systems,” IEEE Autotestcon, Salt Lake Cirty, 8-11 September 2008, pp. 17-22.