Evolutionary Generation of Multi-Tone Sine Stimulus for Analog Circuits Based on Fault Distinguishable Analysis
Jiang Cui · Journal of Astronautics · 2011
In order to improve the distinguish ability of fault samples in analog circuits and enhance the diagnostic performance of fault diagnosis,a novel evolutionary generation method of multi-tone sine stimulus based on fault distinguishable analysis is proposed in this paper.In this method,a multi-tone sine signal is used as the test stimulus.According to the evolutionary generation process of genetic algorithm(GA) dynamic response of the circuit under test(CUT) is implemented by directly modifying stimulated parameters by means of a mixed programming approach.The fault samples are collected dynamically from the test nodes,and then they are preprocessed by using the kernel fuzzy c-means clustering(KFCM) algorithm.The among-class distance between samples in kernel space is calculated and is taken as a standard for the fault distinguish ability.Therefore,the max among-class distance is designed as the optimization goal in the GA algorithm.Experimental results reveal that the optimal multi-tone sine test signal succeeds in improving the diagnosis results.