Automated Diagnostics of Analog Systems Using Fuzzy Logic Approach

This paper presents an automated method for analog system diagnostics, which aims to detect and localize multiple faults in noisy conditions. The generic architecture of the diagnostic scheme and its stages of denoising, stamp extraction, and fault detection are explained. The method is tested on th...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2007-12, Vol.56 (6), p.2175-2185
Main Authors: Bilski, P., Wojciechowski, J.M.
Format: Article
Language:English
Subjects:
Analog systems
Artificial intelligence
Artificial neural networks
Automated
Circuit faults
Diagnostic systems
diagnostics
Dictionaries
Electrical fault detection
Extraction
Fault detection
Faults
Fuzzy logic
Instrumentation
Learning systems
Machine learning
Noise reduction
System testing
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Summary:This paper presents an automated method for analog system diagnostics, which aims to detect and localize multiple faults in noisy conditions. The generic architecture of the diagnostic scheme and its stages of denoising, stamp extraction, and fault detection are explained. The method is tested on three systems of various physical nature. Then, approaches to automated diagnostics of the different classes of the systems are proposed. Machine learning methods (decision-tree-based fuzzy logic) are used to effectively detect faults. Their advantages are explained and confirmed by examples.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2007.908152