Fault Diagnosis of Mixed-Signal Analog Circuit using Artificial Neural Networks

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Ashwani Kumar Narula 1,* Amar Partap Singh 2

1. Electronics & Communication Engineering Section ,Yadavindra College of Engineering, Punjabi University Guru Kashi Campus, Talwandi Sabo, Punjab, India

2. Department of Electronics & Communication Engineering. Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2015.07.02

Received: 22 Dec. 2014 / Revised: 20 Mar. 2015 / Accepted: 2 May 2015 / Published: 8 Jun. 2015

Index Terms

Mixed-Signal Circuit, Sensitivity Analysis, Monte-Carlo Analysis, Artificial Neural Network, Virtual Instrument


This paper presents parametric fault diagnosis in mixed-signal analog circuit using artificial neural networks. Single parametric faults are considered in this study. A benchmark R2R digital to analog converter circuit has been used as an example circuit for experimental validations. The input test pattern required for testing are reduced to optimum value using sensitivity analysis of the circuit under test. The effect of component tolerances has also been taken care of by performing the Monte-Carlo analysis. In this study parametric fault models are defined for the R2R network of the digital to analog converter. The input test patterns are applied to the circuit under test and the output responses are measured for each fault model covering all the Monte-Carlo runs. The classification of the parametric faults is done using artificial neural networks. The fault diagnosis system is developed in LabVIEW environment in the form of a virtual instrument. The artificial neural network is designed using MATLAB and finally embedded in the virtual instrument. The fault diagnosis is validated with simulated data and with the actual data acquired from the circuit hardware.

Cite This Paper

Ashwani Kumar Narula, Amar Partap Singh, "Fault Diagnosis of Mixed-Signal Analog Circuit using Artificial Neural Networks", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.7, pp.11-17, 2015. DOI:10.5815/ijisa.2015.07.02


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