基于可拓理论的模拟电路故障诊断方法

Fault Diagnosis of Analog Circuit Based on Extension Theory

针对模拟电路存在较多故障模式的诊断中易出现分类混叠的问题,提出一种基于可拓理论的故障诊断方法;建立定性地描述模拟电路故障诊断的物元模型,引入可拓集合中的关联函数和相关度;将响应信号进行小波分解提取其各层能量作为故障特征,并利用变尺度的混沌遗传算法优化各故障特征的权重系数,最后定量地计算各故障状态的可能程度;利用实验电路将该方法与另外两种诊断方法比较,实验结果表明,该方法故障分类正确率最高,耗时最短,从而可以证明该方法的有效性。

Aiming at overlapped recognition on analog circuit fault diagnosis with large number of fault categories, this paper presented a fault identification based on extension theory. A matter--element model for analog circuit was established and the correlation functions and correlation value in extension set was introduced. The response signal was decomposed by wavelet and the energy of each layer from wavelet was distilled. Weight parameters were optimized by chaotic genetic algorithm of mutative scale. The failure probability of any possible fault state was calculated. The proposed method and another two methods have been tested on a test circuit. Results show that compared with another two methods, classified rate of the proposed method was the highest and the time used was the least. It confirmed the proposed method effective.