Fault Diagnosis of Manifold SVM Analog Circuit based on Particle Swarm Optimization
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摘要: 支持向量机（SVM）一直被广泛应用于分类判别领域，在模拟电路的故障诊断中，电路普遍复杂多样，传统支持向量机只考虑数据类间距离最大化。本文中提出的粒子群优化的流形支持向量机，在保证数据最大类间间隔的同时，使映射在特征空间的数据，能保持原始空间的流形结构。同时将粒子群算法与SVM相结合，对支持向量机中的权重参数优化，使得对故障的诊断率比传统方法提高了2%~6%。通过实验发现，本文方法有效增强了模拟电路故障诊断的精确度。Abstract: The support vector machine (SVM) has been widely used for classification and discrimination, but the traditional SVM only considers the maximum distance between different data classes. To diagnose the fault of a complex and diverse analog circuit, this paper uses the manifold SVM based particle swarm optimization to ensure that the data mapped in the feature space can maintain the manifold structure of the original space while ensuring the maximum interval between different data classes. At the same time, the particle swarm optimization is used to optimize the weight parameters of the SVM, so that the fault diagnosis rate can be improved by 2%~6% compared with the traditional SVM. The experimental results show that this method effectively enhances the accuracy of analog circuit fault diagnosis.
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表 1 电路中软故障模式
故障类型 标称值 故障值 C1/μF ↑ 0.01 0.015 C2/μF ↑ 0.01 0.014 C3/μF ↑ 0.001 0.001 4 C4/μF ↑ 0.001 0.001 5 R1/Ω ↑ 1 000 1 600 R2/Ω ↓ 1 000 400 R3/Ω ↑ 4 000 6 500 R4/Ω ↓ 4 000 1 800 R5/Ω ↑ 10 000 16 000 R6/Ω ↓ 10 000 4 500 R7/Ω ↑ 60 000 95 000 R8/Ω ↓ 60 000 25 000 注:表中符号↑和↓分别表示偏大和偏小故障。 
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表 2 电路故障诊断率
故障类型 SVM方法 LS_SVM方法 本文方法 C1 ↑ 0.800 0 0.860 0 0.880 0 C2 ↑ 0.740 0 0.760 0 0.800 0 C3 ↑ 0.700 0 0.660 0 0.760 0 C4 ↑ 1 1 1 R1 ↑ 0.800 0 0.760 0 0.820 0 R2 ↓ 0.820 0 0.800 0 0.860 0 R3 ↑ 0.880 0 0.920 0 0.920 0 R4 ↓ 0.640 0 0.720 0 0.740 0 R5 ↑ 0.940 0 0.980 0 0.980 0 R6 ↓ 0.960 0 0.980 0 0.980 0 R7 ↑ 0.860 0 0.860 0 0.940 0 R8 ↓ 0.860 0 0.900 0 0.920 0
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