A novel approach of analog circuit fault diagnosis using support vector machines classifier

Abstract

This paper presents a novel approach of diagnosing actual analog circuits using improved support vector machines classifier (SVC) and this method falls into the category of fault dictionary. The stimulus is exerted on the circuit under test (CUT), and then the output responses are collected. Preprocessing technique is used to compress these responses and get feature samples. The fault classifier is based on the conventional “one against rest” SVC, which is then used to train these feature samples. In order to reduce the test time, the label analysis method for this classifier is employed. However, this simple method will generate a refusal area, which is then resolved by the introduction of space distance discriminant analysis and an apparent diagnosis performance improvement is thus achieved. Two actual experiments, based on data acquisition card (DAC) and digital signal processor (DSP) system respectively are demonstrated to validate the proposed method.