Analog Circuit Fault Detection using Soft Computing Techniques K. Gunanandhini Electronics and Communication Engineering Velalar College of Engineering and Technology Erode, India P. Jayachandar Electronics and Communication Engineering Velalar College of Engineering and Technology Erode, India S. P. Karthi Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore,India Abstract— A new method for detecting faults in analog circuits is proposed. In the proposed method, the fault present in the circuit is detected using system parameters such as location of poles and values of magnitude and phase. The CUT is simulated under various faulty conditions and based on that a fault dictionary is created. Fault classification is done using Fuzzy Inference System (FIS). The CUT used is sallen-key band pass filter. Keywords : Poles. Magnitude And Phase. Fault Dictionary. Fuzzy Inference System. Sallen-Key Filter. I. INTRODUCTION Parametric Fault detection and identification has been an active research topic in recent years and gained a wide attention in the field of analog circuit testing . Detection of faults depends on the response of faulty circuits being suf iciently different from the fault-free response. The definition of sufficiency might be arbitrary thresholds of around 10% from the original values. Analog circuit fault diagnosis has been addressed by two methods: simulate-before-test (SBT) and simulate-after-test (SAT).SBT is based on the use of fault dictionary, which contains responses from simulations of the circuit for different predefined faults. SAT uses measurements to compute parameters of the circuits solving a set of fault diagnosis equations. All computations occur after measurements are acquired. Faults in analog circuits are categorized as catastrophic and parametric faults. Catastrophic faults are due to open and short circuits, caused by sudden and large variations of components. The parametric faults are reported to the circuit functionality. Thus, the value of parameters deviates continuously with time or with environmental conditions to an unacceptable value. Analog fault diagnosis usually consists of three stages which respectively address three important problems in the analog testing and diagnosis: Fault detection – during that we have to find out if the circuit under test is faulty or not; Fault location – which has as purpose to identify where the faulty parameters are; Parameter evaluation – to tell how much the parameter deviations from nominal values are. The analog circuit faults can be broadly classified into catastrophic fault and parameter fault. The catastrophic fault would change the circuit network, and then the transfer fu nction of the CUT is also changed according to the circuit network. There by it is not appropriate to diagnose hard fault using parameter identification method. Some of the soft faults also can't be diagnosed. For example for two series resistances, if the parameter value of one resistance become larger and the other become smaller or vice versa the total parameter value of the two resistors remain unchanged, so the output also remains constant. Therefore parameters are grouped into several modules for the CUT. In recent years, the number and variety of applications of fuzzy logic have increased significantly. Mamdani's fuzzy inference method is the most commonly seen fuzzy methodology. In this paper a new method to diagnose component level parametric faults is attempted . Two different signatures are used to detect the parametric faults present in the CUT. Both single fault and double faults are attempted in this project.The structure of this paper is presented as follows: section 2 outlines the fault diagnosis framework of the proposed method. Section 3 deals with the proposed method. section 4 discusses the results and discussion of the proposed method. Section 65concludes the proposed fault diagnosis method. II. FAULT DIAGNOSIS FRAME WORK The basic idea is to derive the transfer function of the filter. Once the transfer function is derived the signature values are extracted from the fault free condition of the CUT. Then faults are injected in the CUT and the signature values are extracted. Vol. 5 Issue 05, May-2016 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 http://www.ijert.org IJERTV5IS050961 (This work is licensed under a Creative Commons Attribution 4.0 International License.) Published by : 833