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Controlling the Complexity in ModelBased Diagnosis
 Annals of Mathematics and Artificial Intelligence
, 1993
"... We present IDA  an Incremental Diagnostic Algorithm which computes minimal diagnoses from diagnoses, and not from conflicts. As a consequence of this, and by using different models, one can control the computational complexity. In particular, we show that by using a model of the normal behavior, ..."
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Cited by 14 (3 self)
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We present IDA  an Incremental Diagnostic Algorithm which computes minimal diagnoses from diagnoses, and not from conflicts. As a consequence of this, and by using different models, one can control the computational complexity. In particular, we show that by using a model of the normal behavior, the worstcase complexity of the algorithm to compute the k + 1st minimal diagnosis is O(n 2k ), where n is the number of components. On the practical side, an experimental evaluation indicates that the algorithm can efficiently diagnose devices consisting of a few thousand components. We propose to use a hierarchy of models: first a structural model to compute all minimal diagnoses, then a normal behavior model to find the additional diagnoses if needed, and only then a fault model for their verification. IDA separates model interpretation from the search for minimal diagnoses in the sense that the model interpreter is replaceable. In particular, we show that in some domains it is advan...
ModelBased Diagnosis: An Overview
 In Advanced Topics in Artificial Intelligence
, 1992
"... Diagnosis is an important application area of Artificial Intelligence. First generation expert diagnostic systems had exhibited difficulties which motivated the development of modelbased reasoning techniques. Modelbased diagnosis is the activity of locating malfunctioning components of a system so ..."
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Cited by 6 (0 self)
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Diagnosis is an important application area of Artificial Intelligence. First generation expert diagnostic systems had exhibited difficulties which motivated the development of modelbased reasoning techniques. Modelbased diagnosis is the activity of locating malfunctioning components of a system solely on the basis of its structure and behavior. The paper gives a brief overview of the main concepts, problems, and research results in this area. 1 Introduction Diagnosis is one of the earliest areas in which application of Artificial Intelligence techniques was attempted. The diagnosis of a system which behaves abnormally consists of locating those subsystems whose abnormal behavior accounts for the observed behavior. For example, a system being diagnosed might be a mechanical device exhibiting malfunction, or a human patient. There are two fundamentally different approaches to diagnostic reasoning. In the first, heuristic approach, one attempts to codify diagnostic rules of thumb and p...
Enhancing DesignforTest for Active Analog Filters by Using CLP(R)
 JOURNAL OF ELECTRONIC TESTING
, 1994
"... We describe a computeraided approach to automatic fault isolation in active analog filters which enhances the designfortest (DFT) methodology proposed by Soma (1990). His primary concern was in increased controllability and observability while the fault isolation procedure was sketched only in ge ..."
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Cited by 5 (2 self)
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We describe a computeraided approach to automatic fault isolation in active analog filters which enhances the designfortest (DFT) methodology proposed by Soma (1990). His primary concern was in increased controllability and observability while the fault isolation procedure was sketched only in general terms. We operationalize and extend the DFT methodology by using CLP(R) to model analog circuits and by a modelbased diagnosis approach to implement a diagnostic algorithm. CLP(R) is a logic programming language which combines symbolic and numeric computation. The diagnostic algorithm uses different DFT test modes and results of voltage measurements for different frequencies and computes a set of suspected components. Ranking of suspected components is based on a measure of (normalized) standard deviations from predicted mean values of component parameters. The diagnosis is performed incrementally, in each step reducing the set of potential candidates for the detected fault. Presented case ...
Diagnosing Analog Circuits DesignedforTestability by Using CLP(R)
, 1993
"... Recently, a designfortest (DFT) methodology for active analog filters was proposed with the primary goal in increased controllability and observability. We operationalize and extend the DFT methodology by using CLP(!) to model and diagnose analog circuits. CLP(!) is a logic programming language wi ..."
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Recently, a designfortest (DFT) methodology for active analog filters was proposed with the primary goal in increased controllability and observability. We operationalize and extend the DFT methodology by using CLP(!) to model and diagnose analog circuits. CLP(!) is a logic programming language with the capability to solve systems of linear equations and inequalities. It is well suited to model parameter tolerances and to diagnose soft faults, i.e., deviations from nominal values. The diagnostic algorithm uses different DFT test modes and voltage measurements at different frequencies to compute a set of suspected components. Ranking of suspected components is based on a measure of (normalized) standard deviations from This is an extended version of the paper that appears in the Working notes Fourth Intl. Workshop on Principles of Diagnosis, DX93, pp. 105120, University of Wales, Aberystwyth, September 68, 1993. predicted mean values of component parameters. Presented case stu...
Interval Arithmetic With
"... We describe two extensions of CLP(!), motivated by an application to modelbased diagnosis of active analog filters. The first extension addresses the problem of rounding errors in CLP(!). We represent !eals with floatingpoint intervals which are computed by outward rounding. The second extension in ..."
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We describe two extensions of CLP(!), motivated by an application to modelbased diagnosis of active analog filters. The first extension addresses the problem of rounding errors in CLP(!). We represent !eals with floatingpoint intervals which are computed by outward rounding. The second extension increases the expressiveness of linear CLP(!). Constants in linear expressions can now be intervals, which enables reasoning with imprecise model parameters (tolerances). Bounds (sup and inf) for individual variables are computed by the linear optimization via modified Simplex. Both extensions are implemented in a CLP shell  an adaptation of SICStus Prolog, which allows for easy and fast developments and modifications of CLP languages. 1 Introduction The motivation for this work was the idea to use CLP(!) for the simulation and diagnosis of analog circuits. First experiments [Mozeti c et al., 1991] showed that CLP(!) has some advantages over classical simulation tools (like SPICE or Micro...