n this paper a fault diagnosis technique, which employs neural networks to analyze signatures of analog circuits, is proposed. Radial basis functions networks (RBFN) are used to process circuit input–output measurements, and to perform soft fault location. Both noise and effect of parameter variations in the tolerance ranges of non-faulty components are taken into account. The network is trained with circuit signatures, obtained by measuring and coding both circuit input and output signals, which are contained in a ‘fault dictionary’. In this context RBFN architecture is selected, because it is able to cope with ‘new fault’ conditions not well represented in the fault dictionary used for network training. The RBFN classifier was applied to linear and non-linear sample circuits, considering faults both at sub-system level and at component level. Simulations and experimental results show that the developed nets succeeded in classifying faults. The nets trained with single faults has in many cases detected also multiple faults.

Catelani, M., & Fort, A. (2000). Fault diagnosis of electronic analog circuits using a radial basis function network classifier. MEASUREMENT, 28, 147-158 [10.1016/S0263-2241(00)00008-7].

Fault diagnosis of electronic analog circuits using a radial basis function network classifier

FORT, ADA
2000

Abstract

n this paper a fault diagnosis technique, which employs neural networks to analyze signatures of analog circuits, is proposed. Radial basis functions networks (RBFN) are used to process circuit input–output measurements, and to perform soft fault location. Both noise and effect of parameter variations in the tolerance ranges of non-faulty components are taken into account. The network is trained with circuit signatures, obtained by measuring and coding both circuit input and output signals, which are contained in a ‘fault dictionary’. In this context RBFN architecture is selected, because it is able to cope with ‘new fault’ conditions not well represented in the fault dictionary used for network training. The RBFN classifier was applied to linear and non-linear sample circuits, considering faults both at sub-system level and at component level. Simulations and experimental results show that the developed nets succeeded in classifying faults. The nets trained with single faults has in many cases detected also multiple faults.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/32247
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