An Inference Algorithm for Electronic System Diagnostics at the Block Diagram Level

Celso Bation Co

doi:10.20965/jaciii.2006.p0168

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JACIII Vol.10 No.2 pp. 168-172

doi: 10.20965/jaciii.2006.p0168

(2006)

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An Inference Algorithm for Electronic System Diagnostics at the Block Diagram Level

Celso Bation Co

Electronics, Computer and Communication Engineering Department
School of Science and Engineering, Loyola School, Ateneo De Manila University, Loyola Heights, Diliman, Quezon City 1108, Philippines

Received:

February 8, 2005

Accepted:

September 22, 2005

Published:

March 20, 2006

Keywords:

symptom, cause, block diagram, diagnostics

Abstract

Mathematical processes are designed to enable computers to emulate human inference in troubleshooting. Crisp set operations facilitate the interactive handling of incomplete but precise input information. Fuzzy set operations handle imprecise but complete information. The algorithm design we discuss here is limited to crisp set operations. We use diagnostics at the electronic system block diagram level to illustrate inference algorithm methodology. The algorithm deduces root causes and excludes intermediary causes in its conclusions. It also provides information for anticipating subsequent moves. We also consider results providing no conclusion, as is normally experienced by human troubleshooters under certain conditions.

Cite this article as:

C. Co, “An Inference Algorithm for Electronic System Diagnostics at the Block Diagram Level,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.2, pp. 168-172, 2006.

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References

[1] J. Giarratano, and G. Riley, “Expert Systems: Principles and Programming,” PWS Publishing Company, 20 Park Plaza, Boston, MA 002116-4324, 1994.
[2] G. F. Luger, “Artificial Intelligence: Structures and Strategies for Complex Problem Solving,” 4^th Edition, Philippine Edition by Pearson Education South Asia PTE LTD., 2003.
[3] P. Suppes, “Axiomatic Set Theory,” Dover Publications, Inc., 1972.
[4] C. Co, “A Diagnostic Algorithm for Troubleshooting of Electronic Systems At Block Diagram Level,” Second Humanoid, Nanotechnology, Information Technology, Communication and Control Environment and Management (HNICEM) International Conference, The Institute of Electrical and Electronics Engineers Inc., (IEEE)-Philippine Section, March 17-20, 2005.
[5] P. Gmytrasiewicz, J. A. Hassberger, and J. C. Lee, “Fault tree based diagnostics using fuzzy logic,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol.12, Issue 11, pp. 1115-1119, Nov., 1990.
[6] F. Lim, and C. Co, “A Set Theory Model for Process and Equipment Diagnosis,” 2^nd National Electronics and Communication Engineering Conference Philippine, Nov., 2002.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] J. Giarratano, and G. Riley, “Expert Systems: Principles and Programming,” PWS Publishing Company, 20 Park Plaza, Boston, MA 002116-4324, 1994.

[2] [2] G. F. Luger, “Artificial Intelligence: Structures and Strategies for Complex Problem Solving,” 4^th Edition, Philippine Edition by Pearson Education South Asia PTE LTD., 2003.

[3] [3] P. Suppes, “Axiomatic Set Theory,” Dover Publications, Inc., 1972.

[4] [4] C. Co, “A Diagnostic Algorithm for Troubleshooting of Electronic Systems At Block Diagram Level,” Second Humanoid, Nanotechnology, Information Technology, Communication and Control Environment and Management (HNICEM) International Conference, The Institute of Electrical and Electronics Engineers Inc., (IEEE)-Philippine Section, March 17-20, 2005.

[5] [5] P. Gmytrasiewicz, J. A. Hassberger, and J. C. Lee, “Fault tree based diagnostics using fuzzy logic,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol.12, Issue 11, pp. 1115-1119, Nov., 1990.

[6] [6] F. Lim, and C. Co, “A Set Theory Model for Process and Equipment Diagnosis,” 2^nd National Electronics and Communication Engineering Conference Philippine, Nov., 2002.