AI-TEST-a real life expert system for electronic troubleshooting (a description and a case study)

Ben-Bassat, Moshe; Ben-Arie, D.; Beniaminy, Israel; Cheifetz, J.; Klinger, M.

doi:10.1109/caia.1988.196074

Cited by 14 publications

(2 citation statements)

References 5 publications

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“…Recently a number of software products have emerged which attempt to automate electronic troubleshooting [4,5,9]. Most of these approaches are based on the use of component connectivity.…”

Section: Introductionmentioning

confidence: 99%

Automated diagnosis of analog circuits

Tong¹,

Zalondek²,

Jolly³

1989

Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Exper

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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 modelbased 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.

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“…Recently a number of software products have emerged which attempt to automate electronic troubleshooting [4,5,9]. Most of these approaches are based on the use of component connectivity.…”

Section: Introductionmentioning

confidence: 99%

Automated diagnosis of analog circuits

Tong¹,

Zalondek²,

Jolly³

1989

Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Exper

View full text Add to dashboard Cite

show abstract

“…This provided us with the incentive to consolidate MBR with CBR in order to gain the best of both worlds. Our research along these lines resulted in the Artificial Intelligence-CBR (AITEST-CBR) equipment diagnosis expert system, continuing our earlier research on the AITEST expert system, Ben-Bassat, et ai, 1988 [8]. The results of this research, which we present in the following sections, are very encouraging.…”

Section: Analogy To Medicinementioning

confidence: 49%

Can model-based and case-based expert systems operate together?

Ben-Bassat¹,

Beniaminy²,

Joseph³

1998 IEEE AUTOTESTCON Proceedings. IEEE Systems Readiness Technology Conference. Test Technology for the 21st Century (Cat. No.

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In discussing ongoing debate as to whether to case-based systems, or vice that there is no real need far mutually exclusive and, to thgt diagnostic expert systems, there is an model-based systems are superior versa. Our experience has shown debate because the two are-not contrary, complement each other.knowledge bases. Nun started all over the woi-Id systems were successkIn this paper we will dis' uss the role of expert systems incombining model-based and case-based reasoning to effect the most efficient user defined solution to diagnostic performance.

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Advanced Diagnostics and Artificial Intelligence

Blumberg¹

2004

Clinical Engineering Handbook

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AI-TEST-a real life expert system for electronic troubleshooting (a description and a case study)

Abstract: A I T E S T is a real life expert s y s t e m for functional fault detection and isolation in electronic equipment.

Cited by 14 publications

References 5 publications

Automated diagnosis of analog circuits

Automated diagnosis of analog circuits

Can model-based and case-based expert systems operate together?

Advanced Diagnostics and Artificial Intelligence

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