Abstract
studies have been presented for the fault diagnosis of electronic analog circuits with worst case fault models using ±50% variation in the parametric values of the components. The study of for parametric fault detection in electronic analog circuits -faults as small as 10% or less was uncovered. The use of the neural network for parametric fault diagnosis in an analog circuit, based upon the polynomial curve fitting coefficients of the output response of an analog circuit is presented in this study. Building upon the theory of polynomial coefficients we propose a parametric fault diagnosis methodology. A polynomial of suitable degree is fitted to the output frequency response of an analog circuit. The coefficients of the polynomial attain different values under faulty and non faulty conditions. Using these features of polynomial coefficients, a BPNN is used to detect the parametric faults. Simulation results are presented for a benchmark bi quad filter circuit. Single resistance and capacitance faults of ±1% to ±50% deviation from nominal values were correctly diagnosed. KeywordsNetwork, Parametric faults, Analog circuit, Curve fitting, Polynomial coefficients.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.