An Artificial Intelligence Approach to Analog Systems Diagnosis

Pipitone, Frank; DeJong, Kenneth; Spears, William M.

doi:10.1007/978-1-4615-9747-6_7

Cited by 19 publications

(11 citation statements)

References 3 publications

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“…Thanks to the advances in computational intelligence in the last decades new diagnostic paradigms have been applied based on: model-based concepts [1]; production rule based artificial intelligence [7]; ANNs [8]; genetic algorithms [9]; and fuzzy-reasoning [10]; all trying to create an approach that exhibits properties that we might consider to be "intelligent behavior". A comprehensive overview of the complete subject of diagnosis of analog electronic circuit may be found in [11].…”

Section: Processmentioning

confidence: 99%

Hierarchical approach to diagnosis of electronic circuits using ANNs

Stošović

Litovski

2010

10th Symposium on Neural Network Applications in Electrical Engineering

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 Abstract-In this paper, we apply artificial neural networks (ANNs) to the diagnosis of a mixed-mode electronic circuit. In order to tackle the circuit complexity and to reduce the number of test points hierarchical approach to the diagnosis generation was implemented with two levels of decision: the system level and the circuit level. For every level, using the simulation-before-test (SBT) approach, fault dictionary was created first, containing data relating the fault code and the circuit response for a given input signal. Also, hypercomputing was implemented, i.e. we used parallel simulation of large number of replicas of the original circuit with faults inserted to achieve fast creation of the fault dictionary. ANNs were used to model the fault dictionaries. At the topmost level, the fault dictionary was split into parts simplifying the implementation of the concept. During the learning phase, the ANNs were considered as an approximation algorithm to capture the mapping enclosed within the fault dictionary. Later on, in the diagnostic phase, the ANNs were used as an algorithm for searching the fault dictionary. A voting system was created at the topmost level in order to distinguish which ANN output is to be accepted as the final diagnostic statement. The approach was tested on an example of an analog-to-digital converter.

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Section: Processmentioning

confidence: 99%

Hierarchical approach to diagnosis of electronic circuits using ANNs

Stošović

Litovski

2010

10th Symposium on Neural Network Applications in Electrical Engineering

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“…Specific design for testability concepts can be applied. Thanks to the advances in computational intelligence in the last decades new diagnostic paradigms have been applied based on: model-based concepts (Benjamins & Jansweijer, 1990); production rule based artificial intelligence (Pipitone et al, 1991); ANNs (Hayashi et al, 2002); genetic algorithms (Golonek & Rutkowski, 2002); and fuzzy-reasoning (Pous, et al, 2002); all trying to create an approach that contains properties that we might consider to be "intelligent behaviour". In order to get an idea of why and how ANNs are applied to analogue electronic circuit diagnosis, the diagnostic concept ( Fig.…”

Section: Concepts Of Diagnosismentioning

confidence: 99%

“…This means that inputs, outputs, rules and the corresponding operators to combine them were defined. A production rule based concept was reported in (Pipitone et al, 1991). ANNs have previously been applied to diagnosis (Spina & Upadhyaya, 1997;Materka, 1994;Rodrigez et al, 1994;Aminian & Aminian, 2000;He et al, 2002;Andrejević & Litovski, 2004;Aminian et al, 2002;Stopjakova et al, 2004;Yu et al, 1994;Collins et al, 1994;Catelani & Gori, 1996;Maidon et al, 1997;Yang et al, 2000).…”

Section: Diagnosis Of Nonlinear Dynamic Analogue Circuitsmentioning

confidence: 99%

Electronic Circuits Diagnosis Using Artificial Neural Networks

Stošović¹,

Litovski²

2009

Micro Electronic and Mechanical Systems

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“…FIS [23], uses qualitative causal models to describe the unit under test. It is suitable to deal with analog systems but it could suffer from its qualitative structure when treating analog circuits.…”

Section: Analog Circuit Diagnosis: Some Famous Systemsmentioning

confidence: 99%

Test and diagnosis of analog circuits: When fuzziness can lead to accuracy

Mohamed

Marzouki

1996

J Electron Test

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Editor: B. Kaminska Abstract.Testing and diagnosing analog circuits is a very challenging problem. The inaccuracy of measurement and the infinite domain of possible values are the principal difficulties. AI approaches were the base of many systems which tried to overcome these problems. The first part of this paper is a state of the art of this research area. We present two fundamental approaches, model-based reasoning and qualitative reasoning, and the systems implementing them; a discussion and an evaluation of these systems are given. In the second part, we present and propose a novel approach based on fuzzy logic in order to go further in dealing with analog circuits testing and diagnosis. Tolerance is treated by means of fuzzy intervals which are more general, more efficient and of higher fidelity to represent the imprecision in its different forms than other approaches. Fuzzy intervals are also able to be semi-qualitative which is more suitable to the simulation of analog systems. We use this idea to develop a best test point finding strategy based on fuzzy probabilities and fuzzy decision-making methodology. Finally, a complete expert system which implements this approach is presented.

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An Artificial Intelligence Approach to Analog Systems Diagnosis

Cited by 19 publications

References 3 publications

Hierarchical approach to diagnosis of electronic circuits using ANNs

Hierarchical approach to diagnosis of electronic circuits using ANNs

Electronic Circuits Diagnosis Using Artificial Neural Networks

Test and diagnosis of analog circuits: When fuzziness can lead to accuracy

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