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.
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This article is the synthesis of work carried out jointly with O. Jehl and O. Raiman from IBM France and P. Devès, P. Luciani and P. Taillibert from Dassault Electronique. Parts of this article come from the papers [1–3] and my thanks go to AAAI Press, Wiley and Morgan Kaufmann for permission to reprint these extracts.
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Dague, P. Model-based diagnosis of analog electronic circuits. Ann Math Artif Intell 11, 439–492 (1994). https://doi.org/10.1007/BF01530755
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DOI: https://doi.org/10.1007/BF01530755