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Model-based diagnosis of analog electronic circuits

Model-based diagnosis of analog electronic circuits Diagnosing analog systems, i.e. systems for which physical quantities vary over time in a continuous range is, in itself, a difficult problem. Analog electronic circuits, especially those with feedback loops, raise new difficulties that cannot be solved by using classical techniques. This paper shows how model-based diagnosis theory can be used to diagnose analog circuits. The two main tasks for making the theory applicable to real size problems will be emphasized: the modeling of the system to be diagnosed, and the building of efficient conflict recognition engines adapted to the formalism used for the modeling. This will be illustrated through the description of two systems. The first one, DEDALE, only considers failures observable in quiescent mode. It uses qualitative modeling based on relative orders of magnitude relations, for which an axiomatics is given, thus allowing a symbolic solver for checking consistency of such relations to be developed. The second one, CATS/DIANA, deals with time variations. It uses modeling based on numeric intervals, arrays of such intervals to represent transient signals, and an ATMS-like domain-independent conflict recognition engine, CATS. This engine is able to work on such data and to achieve interval propagation through constraints in such a way as to focus on the detection of all minimal nogoods. It is thus well adapted for diagnosing continuous time-varying physical systems. Experimental results of the two systems are given through various types of circuits. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Model-based diagnosis of analog electronic circuits

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References (27)

Publisher
Springer Journals
Copyright
Copyright
Subject
Computer Science; Artificial Intelligence; Mathematics, general; Computer Science, general; Complex Systems
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1007/BF01530755
Publisher site
See Article on Publisher Site

Abstract

Diagnosing analog systems, i.e. systems for which physical quantities vary over time in a continuous range is, in itself, a difficult problem. Analog electronic circuits, especially those with feedback loops, raise new difficulties that cannot be solved by using classical techniques. This paper shows how model-based diagnosis theory can be used to diagnose analog circuits. The two main tasks for making the theory applicable to real size problems will be emphasized: the modeling of the system to be diagnosed, and the building of efficient conflict recognition engines adapted to the formalism used for the modeling. This will be illustrated through the description of two systems. The first one, DEDALE, only considers failures observable in quiescent mode. It uses qualitative modeling based on relative orders of magnitude relations, for which an axiomatics is given, thus allowing a symbolic solver for checking consistency of such relations to be developed. The second one, CATS/DIANA, deals with time variations. It uses modeling based on numeric intervals, arrays of such intervals to represent transient signals, and an ATMS-like domain-independent conflict recognition engine, CATS. This engine is able to work on such data and to achieve interval propagation through constraints in such a way as to focus on the detection of all minimal nogoods. It is thus well adapted for diagnosing continuous time-varying physical systems. Experimental results of the two systems are given through various types of circuits.

Journal

Annals of Mathematics and Artificial Intelligence – Springer Journals

Published: Apr 5, 2005

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