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A Preprocessor for Analog Circuit Fault Diagnosis Based on Prony's Method

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The paper presents a new technique for analog circuit fault diagnosis. The circuit under test is simulated both at fault free and different faulty conditions using PSPICE software. The AC response to a set of sinusoidal input frequencies is calculated at selected test nodes. Prony's method is then utilized as a preprocessor to extract an optimal set of features representing nodal voltage waveforms. The resultant features are used to train a back-propagation neural network to identify circuit faults. The potential of the algorithm is demonstrated by a second order active circuit example.

Keywords: Analog circuits; Fault diagnosis; Neural networks; Prony's method

Document Type: Original Article


Affiliations: 1: Electronics and Communication Department, Cairo University, Giza, Egypt. E-mail: 2: Engineering Mathematics and Physics Department, Cairo University, Giza, 1221, Egypt. E-mail:

Publication date: January 1, 2003


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