文档介绍：采用模糊支持向量机的模拟电路故障诊断新方法*
唐静远师奕兵
(电子科技大学自动化工程学院, 成都 610054)
摘要: 为了解决模拟电路故障诊断复杂多样难于辨识的问题, 有效提高分类的准确度, 提出了一种模拟电路故障诊断的新方法。首先对采集的信号进行时-频域联合特征提取并采用新的模糊隶属度函数确定训练样本的隶属度, 消除噪声和野点对故障诊断的影响; 然后将训练集输入模糊支持向量机分类方法训练获得故障诊断模型; 最后将测试集输入训练好的模糊支持向量机分类模型, 实现对不同故障类型的识别。将该方法应用于CTSV滤波电路进行故障诊断仿真实验, 结果显示该方法不仅能正确分类单故障而且能有效分类多故障, %, 为模拟电路故障诊断开辟新的途径。
关键词: 特征提取;模糊支持向量机;模拟电路;故障诊断
中图分类号: TN707 文献标识码: A 国家标准学科分类代码:
New method of analog circuit fault diagnosis using fuzzy support vector machine
Tang Jingyuan Shi Yibing
(School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China)
Abstract: In order to solve the problem of correctly identifying fault classes in analog circuit fault diagnosis and improve classification ability, a novel fault diagnosis method based on fuzzy support vector machine (FSVM) is proposed in this paper. Firstly, the fault feature vectors are extracted by joint time-frequency domain feature extraction method and the fuzzy membership of the feature vectors puted by a novel proposed membership function to e the sensitivity to noise and outliers. Then, after training the FSVM by faulty feature vectors, the FSVM model of the circuit fault diagnosis system is built. Finally, test samples’ feature vectors are input into the trained FSVM model to identify the different fault cases. The simulation results for anal