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Novel Analog Circuit Fault Detection using Adaptive Neuro Fuzzy Inference System Shanthi M.*, Bhuvaneswari M. C.** *Electronics and Communication Engineering, Kumaraguru College of Technology, Coimbatore, India. shanthi.m.ece@kct.ac.in **Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, India. mcb@eee.psgtech.ac.in Online published on 23 March, 2017. Abstract Testing issues are gaining importance with the quick development of both digital and analog circuit industry. Fault diagnosis is an essential way for development and maintenance of safe and reliable electronic circuits and systems. This paper introduces new method for detecting faults in analog circuits. An Adaptive Neuro Fuzzy Inference System (ANFIS) is built to classify the faults in analog circuit. The Circuit Under Test (CUT) with the nominal component values is considered. The transfer function of the CUT is derived and used to make a measurement that characterizes the CUT. The behavior of the CUT is measuredthrough poles and zeros of the transfer function under fault free and various faulty conditions. Monte-Carlo technique is used to extract the data corresponding to the various faulty conditions. These measurements are utilized to build ANFIS based classifier and trained for the CUT. A benchmark circuit sallen key band pass filter is tested to visualize the high classification performance of the proposed procedure. Top Keywords Analog circuits, fault diagnosis, fuzzy inference system, Neural Network. Top | |
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