Automated diagnosis of analog circuits

ABSTRACT

While a number of different approaches have been proposed to automatically troubleshoot electronic systems given schematic information, few are sufficiently powerful to tackle the complexity of analog circuits at the resistor/transistor level. This paper describes work which applies quantitative model-based reasoning techniques to this problem. The circuit schematic is converted into a constraint diagram to which the combination of constraint propagation and dependency tracking are applied to search for inconsistencies and identify the implicated components. Instead of resorting to the propagation of symbols, the technique of aggregate models is used to enhance deductive power but with manageable computation. These ideas have been implemented in the program FIX which diagnoses a given circuit by recognizing inconsistencies among measurements, identifies the set of fault candidates and their posterior probabilities, and suggests the best next measurement. Modeling and inference issues are discussed, and diagnosis of various faults in an example circuit by FIX is described.

REFERENCES

• 1: Duhamel, P., and Rault, J. C., "Automatic Test Generation Techniques for Analog Circuits and Systems: A Review", IEEE Transactions on Circuits and Systems, CAS-26(1979) 411-440.
• 2: Randall Davis, Diagnostic reasoning based on structure and behavior, Artificial Intelligence, v.24 n.1-3, p.347-410, Dec. 1984 [doi>10.1016/0004-3702(84)90042-0]
• 3: Michael R. Genesereth, The use of design descriptions in automated diagnosis, Artificial Intelligence, v.24 n.1-3, p.411-436, Dec. 1984 [doi>10.1016/0004-3702(84)90043-2]
• 4: Cantone, R.R., "IN-ATE: Fault Diagnosis as Expert System Guided Search", paper to appear in Computer Expert Systems, L. Bolc and M.J. Coombs(eds.), Springer-Verlag, Heidelberg, 1987.
• 5: Frank Pipitone, The FIS electronics troubleshooting system, Computer, v.19 n.7, p.68-76, July 1986 [doi>10.1109/MC.1986.1663281]
• 6: J de Kleer , B C Williams, Diagnosing multiple faults, Artificial Intelligence, v.32 n.1, p.97-130, April 1987 [doi>10.1016/0004-3702(87)90063-4]
• 7: Geffner H. and Pearl J., "Distributed Diagnosis of Systems with Multiple Faults", Proceedings of the IEEE Third Conference on AI Applications(1987), 156- 162.
• 8: Dague P., Raiman O., and Deves P., "Troubleshooting: When Modeling is the Trouble", Proceedings of the AAM Conference(1987), 600- 605.
• 9: Ben-Bassat, M., et. al., "AI-TEST A Real Life Expert System for Electronic Troubleshooting", Proceedings of the Fourth Conference on Artificial Intelligence Applications(1987), 2-10.
• 10: Brown, J. S., Burton, R. R., and de Kleer, J., "Pedagogical, Natural Language and Knowledge Engineering Techniques in SOPHIE i, II, and III", in D. Sleeman and J. S. Brown (Eds.), Intelligent Tutoring Systems, Academic Press, New York(1982), 227- 282.
• 11: Sussman, G. J.,and Steele, G. L. Jr., "CONSTRAINTS - A Language for Expressing Almost-Hierarchical Descriptions", Artificial Intelligence, 14(1980) 1-39.
• 12: Ernest Davis, Constraint propagation with interval labels, Artificial Intelligence, v.32 n.3, p.281-331, July 1987 [doi>10.1016/0004-3702(87)90091-9]
• 13: Patfipati, K. R., Deckert, J. C., and Alexandridis, M. G., "Time-Efficient Sequencer of Tests (TEST)", Proceedings of the 1985 IEEE Autotestcon, 49-62.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE

Additional Classification:
  B. Hardware
  B.7 INTEGRATED CIRCUITS
      B.7.3 Reliability and Testing**
          Subjects: Error-checking**

  C. Computer Systems Organization
  C.4 PERFORMANCE OF SYSTEMS
      Subjects: Measurement techniques

  J. Computer Applications
  J.2 PHYSICAL SCIENCES AND ENGINEERING
      Subjects: Electronics

General Terms:
Human Factors, Measurement, Reliability, Theory