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Qualitative dynamic diagnosis of circuits

Published online by Cambridge University Press:  27 February 2009

Alessandra Fanni ,
Paolo Diana ,
Alessandro Giua  and
Marco Perezzani
Alessandra Fanni
Affiliation:
Istituto di Elettrotecnica, Facoltà di Ingegneria Università di Cagliari, Piazza d'Armi, 09123 Cagliari, Italy
Paolo Diana
Affiliation:
Istituto di Elettrotecnica, Facoltà di Ingegneria Università di Cagliari, Piazza d'Armi, 09123 Cagliari, Italy
Alessandro Giua
Affiliation:
Istituto di Elettrotecnica, Facoltà di Ingegneria Università di Cagliari, Piazza d'Armi, 09123 Cagliari, Italy
Marco Perezzani
Affiliation:
Istituto di Elettrotecnica, Facoltà di Ingegneria Università di Cagliari, Piazza d'Armi, 09123 Cagliari, Italy
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Abstract

We describe ACDS, an automatic diagnostic system. ACDS is capable of diagnosing faults on analog circuits in dynamic conditions. The circuit's dynamic behavior is studied by means of a series of intrastate simulations during which the qualitative state of the circuit does not change. An acquistion board collects the value of a set of quantities corresponding to accessible test points. These measurements are converted into qualitative values and are used for two purposes: first, to determine the state of the circuit components; second, to trigger the diagnostic procedure whenever a discrepancy between observed and predicted behavior is found. The main difficulty in this phase of measurement interpretation is in obtaining meaningful numerical-qualitative data conversion for values of quantities approaching a boundary between two different qualitative intervals. System performance has been verified through a number of simulations, which have shown the proposed approach to be efficient both in terms of localized faults and of flexibility in adapting to different circuits.

Type
Research Article
Information
AI EDAM , Volume 7 , Issue 1 , February 1993 , pp. 53 - 64
Copyright
Copyright © Cambridge University Press 1993

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References

Bandler, J. W. and Salama, A. E. 1985. Fault diagnosis of analog circuits. Proceedings of the IEEE 73 (8), 1279–1325.Google Scholar
Cois, A., Fanni, A., Giua, A. and Pes, S. 1989. Qualitative analysis in simulating analog circuits. Proceedings of the IEEE International Symposium on Intelligent Control (Albany, NY), pp. 531–536.Google Scholar
Cunningham, F. and Brady, M. 1987. Qualitative reasoning in electronic fault diagnosis. Proceedings of the 10th International Joint Conference on Artificial Intelligence (Milan, Italy), pp. 443–445.Google Scholar
Dague, Ph., Devès, Ph., and Raiman, O. 1987. Troubleshooting: when modeling is the trouble. Proceedings of the AAAI 87 Conference (Seattle, Washington), pp. 600–605.Google Scholar
Dague, Ph., Jehl, O., Devès, Ph., Luciani, P. and Taillibert, P. 1991. When oscillators stop oscillating. Proceedings of the 12th International Joint Conference on Artificial Intelligence (Sydney, Australia), pp. 1109–1115.Google Scholar
DeCoste, D. 1991. Dynamic across-time measurement interpretation. Artificial Intelligence 51, 273–341.Google Scholar
deKleer, J. 1984. How circuits work. Artificial Intelligence 24, 205–280.CrossRefGoogle Scholar
deKleer, J. and Brown, J. S. 1984. A qualitative physics based on confluences. Artificial Intelligence 24, 7–84.Google Scholar
deKleer, J. and Williams, B. C. 1987. Diagnosing multiple faults. Artificial Intelligence 32, 97–129.Google Scholar
Diana, P., Fanni, A. and Perezzani, M. 1991. Qualitative modeling in simulation of analog circuits. Proceeding of the 10th IASTED International Symposium on Modeling, Identification and Control (Innsbruck, Austria), pp. 372–375.Google Scholar
Dvorak, D. and Kuipers, B. 1989. Model-based monitoring of dynamic systems. Proceedings of the 11th International Joint Conference on Artificial Intelligence (Detroit, Michigan), pp. 1238–1242.Google Scholar
Fanni, A., Giua, A. and Manca, M. G. 1988. Automated diagnosis for digital circuits, in T., O'Shea and V., Sgurev (ed), Artificial Intelligence III: Methodology, Systems, Applications, North Holland: Elsevier Science Publishers, pp. 373–379.Google Scholar
Fanni, A., Diana, P. and Perezzani, M. 1992. Intelligent data acquisition for qualitative fault diagnosis of analog circuits. Proceedings of the Singapore International Conference on Intelligent Control and Instrumentation, pp. 75–80.Google Scholar
Forbus, K. D. 1984. Qualitative process theory. Artificial Intelligence 24, 86–168.Google Scholar
Giovannini, F. and Malabocchia, F. 1985. A model based expert system for HW troubleshooting driven by compiled control knowledge. Expert Systems '85, pp. 65–67.Google Scholar
Hochwald, W. and Bastian, J. D. 1979. A DC approach for analog fault dictionary determination. IEEE Transactions on Circuits and Systems CAS-26, 523–529.Google Scholar
IEEE. 1979. IEEE Transactions on Circuits and Systems, Special Issue on Automatic Fault Diagnosis, CAS-26, (7).Google Scholar
IEEE. 1987. Selected paper on analog fault diagnosis, in Ruey-Wen, Liu (ed.), Advances in Circuits and Systems, IEEE Circuits and Systems Society, IEEE Press.Google Scholar
Kuipers, B. 1986. Qualitative simulation. Artificial Intelligence 29, 289–338.CrossRefGoogle Scholar
Raiman, O. 1991. Order of magnitude reasoning. Artificial Intelligence 51, 11–37.Google Scholar
Reiter, R. 1987. A theory of diagnosis from first principles. Artificial Intelligence, 31, 57–95.CrossRefGoogle Scholar