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14
A New Bayesian Approach to Multiple Intermittent Fault Diagnosis
, 2009
"... Logic reasoning approaches to fault diagnosis account for the fact that a component cj may fail intermittently by introducing a parameter gj that expresses the probability the component exhibits correct behavior. This component parameter gj, in conjunction with a priori fault probability, is used in ..."
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Cited by 7 (5 self)
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Logic reasoning approaches to fault diagnosis account for the fact that a component cj may fail intermittently by introducing a parameter gj that expresses the probability the component exhibits correct behavior. This component parameter gj, in conjunction with a priori fault probability, is used in a Bayesian framework to compute the posterior fault candidate probabilities. Usually, information on gj is not known a priori. While proper estimation of gj can have a great impact on the diagnostic accuracy, at present, only approximations have been proposed. We present a novel framework, BARINEL, that computes exact estimations of gj as integral part of the posterior candidate probability computation. BARINEL’s diagnostic performance is evaluated for both synthetic and real software systems. Our results show that our approach is superior to approaches based on classical persistent fault models as well as previously proposed intermittent fault models.
Heuristic search for targetvalue path problem
 In First International Symposium on Search Techniques in Artificial Intelligence and Robotics
, 2008
"... In this paper, we define a class of combinatorial search problems in which the objective is to find a set of paths in a graph whose elements ’ value is as close as possible to some target value. Unlike the usual shortest path problem, the goal is not necessarily to find paths with minimum length. W ..."
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Cited by 7 (4 self)
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In this paper, we define a class of combinatorial search problems in which the objective is to find a set of paths in a graph whose elements ’ value is as close as possible to some target value. Unlike the usual shortest path problem, the goal is not necessarily to find paths with minimum length. We show that in most cases it is possible to decompose the problem into components where heuristic search can be used. We demonstrate the benefits of this approach on a synthetic domain and illustrate an instantiation of the approach for a problem in modelbased diagnosis. 1.
FRACTAL: Efficient Fault Isolation Using Active Testing
, 2009
"... ModelBased Diagnosis (MBD) approaches often yield a large number of diagnoses, severely limiting their practical utility. This paper presents a novel active testing approach based on MBD techniques, called FRACTAL (FRamework for ACtive Testing ALgorithms), which, given a system description, compute ..."
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Cited by 6 (3 self)
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ModelBased Diagnosis (MBD) approaches often yield a large number of diagnoses, severely limiting their practical utility. This paper presents a novel active testing approach based on MBD techniques, called FRACTAL (FRamework for ACtive Testing ALgorithms), which, given a system description, computes a sequence of control settings for reducing the number of diagnoses. The approach complements probing, sequential diagnosis, and ATPG, and applies to systems where additional tests are restricted to setting a subset of the existing system inputs while observing the existing outputs. This paper evaluates the optimality of FRACTAL, both theoretically and empirically. FRACTAL generates test vectors using a greedy, nextbest strategy and a lowcost approximation of diagnostic information entropy. Further, the approximate sequence computed by FRACTAL’s greedy approach is optimal over all polytime approximation algorithms, a fact which we confirm empirically. Extensive experimentation with ISCAS85 combinational circuits shows that FRACTAL reduces the number of remaining diagnoses according to a steep geometric decay function, even when only a fraction of inputs are available for active testing.
A depthfirst approach to targetvalue search
"... In this paper, we consider how to improve the scalability and efficiency of targetvalue path search on directed acyclic graphs. To this end, we introduce a depthfirst heuristic search algorithm and a dynamicprogramming method to compute the heuristic’s pattern database in linear (in the number of ..."
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Cited by 2 (0 self)
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In this paper, we consider how to improve the scalability and efficiency of targetvalue path search on directed acyclic graphs. To this end, we introduce a depthfirst heuristic search algorithm and a dynamicprogramming method to compute the heuristic’s pattern database in linear (in the number of edges) time. We show the benefits of the new approach over previous work on this problem (Kuhn et al. 2008a).
A ModelBased Active Testing Approach to Sequential Diagnosis
"... Modelbased diagnostic reasoning often leads to a large number of diagnostic hypotheses. The set of diagnoses can be reduced by taking into account extra observations (passive monitoring), measuring additional variables (probing) or executing additional tests (sequential diagnosis/test sequencing). ..."
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Modelbased diagnostic reasoning often leads to a large number of diagnostic hypotheses. The set of diagnoses can be reduced by taking into account extra observations (passive monitoring), measuring additional variables (probing) or executing additional tests (sequential diagnosis/test sequencing). In this paper we combine the above approaches with techniques from Automated Test Pattern Generation (ATPG) and ModelBased Diagnosis (MBD) into a framework called Fractal (FRamework for ACtive Testing ALgorithms). Apart from the inputs and outputs that connect a system to its environment, in active testing we consider additional input variables to which a sequence of test vectors can be supplied. We address the computationally hard problem of computing optimal control assignments (as defined in Fractal) in terms of a greedy approximation algorithm called Fractal G. We compare the decrease in the number of remaining minimal cardinality diagnoses of Fractal G to that of two more Fractal algorithms: Fractal ATPG and Fractal P. Fractal ATPG is based on ATPG and sequential diagnosis while Fractal P is based on probing and, although not an active testing algorithm, provides a baseline for comparing the lower bound on the number of reachable diagnoses for the Fractal algorithms. We empirically evaluate the tradeoffs of the three Fractal algorithms by performing extensive experimentation on the ISCAS85/74XXX benchmark of combinational circuits. 1.
Kleer, ‘Diagnosis with incomplete models: Diagnosing hidden interaction faults
 in Proc. DX10 Workshop
, 2010
"... Abstract This paper extends modelbased diagnosis (MBD) ..."
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Abstract This paper extends modelbased diagnosis (MBD)
Pervasive Model Adaptation: Integration of Planning and Information Gathering in Dynamic Production Systems
"... Abstract: Modelbased planning often presumes a static system model, while in a practice physical system may evolve or drift over time. This paper proposes the idea of pervasive model adaptation in a production system, where the model is dynamically updated using observation of production output. Th ..."
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Abstract: Modelbased planning often presumes a static system model, while in a practice physical system may evolve or drift over time. This paper proposes the idea of pervasive model adaptation in a production system, where the model is dynamically updated using observation of production output. The core idea is the interplay between model adaptation and production planning. We seek plans which simultaneously serve the goals of achieving high productivity for production, and information gathering for model adaptation. We use a modular printing example to illustrate issues such as formulation of the information criterion and search strategy for informative plans. The idea of pervasive adaptation can be further extended to improve long term productivity in production systems. 1.
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"... framework for continuously estimating persistent and intermittent failure probabilities ..."
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framework for continuously estimating persistent and intermittent failure probabilities
Reducing the Diagnostic Uncertainty of a Paper Input Module by Active Testing
"... Reducing the diagnostic uncertainty of a ..."