| 谈恩民,李 峰.结合LMD云模型和ABC-LSSVM的
模拟电路故障诊断[J].电子测量与仪器学报,2020,34(2):80-87 |
| 结合LMD云模型和ABC-LSSVM的
模拟电路故障诊断 |
| Analog circuit fault diagnosis combined with LMDcloud model and ABC LSSVM |
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| DOI： |
| 中文关键词: 故障诊断 特征提取 云逆向发生器 局域均值分解(LMD)算法 最小二乘支持向量机(LSSVM) |
| 英文关键词:fault diagnosis feature extraction cloud inverse generator local mean decomposition (LMD) algorithm least squares support vector machine (LSSVM) |
| 基金项目:国家自然科学基金（61741403）资助项目 |
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| 摘要点击次数: 578 |
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| 中文摘要: |
| 针对模拟电路存在的非线性和非平稳性，以及电路元件存在容差而带来诊断时的模糊性与随机性等问题，提出了一种结合了局域均值分解(LMD)云模型特征提取和人工蜂群 最小二乘法支持向量机(ABC LSSVM)分类器的模拟电路故障诊断法。首先，利用LMD算法对初始故障信号进行分解，再将分解的信号通过云逆向发生器计算得出分解信号的云数字特征，并将得到的云数字特征构造为故障特征向量。然后，将部分故障特征向量作为测试样本输入到 ABC算法优化的LSSVM中，对各电路故障进行分类识别，得到各故障的分类精度。以两个国际基准电路CTSV和Sallen_Key电路为验证对象，结果表明，该方法提取的故障特征能很好的反映电路各故障状态信息，所提方法的故障诊断精度均达到99%。 |
| 英文摘要: |
| Aiming at the problems of non linearity and non stationarity of analog circuits, and the ambiguity and randomness of diagnostics caused by the tolerance of circuit components, an analog circuit fault diagnosis method combining local mean decomposition (LMD) cloud model feature extraction and ABC LSSVM classifier is proposed. First, the LMD algorithm is used to decompose the initial fault signal, and the cloud digital feature of the decomposed signal is calculated by the cloud inverse generator, and the obtained cloud digital feature is constructed as a fault feature vector. Then, a part of the fault feature vector is input as a test sample into the LSSVM optimized by the artificial bee colony (ABC) algorithm, and the circuit faults are classified and identified to obtain the classification accuracy of each fault. Two international benchmark circuits, CTSV and Sallen_Key, are used as verification objects. The results show that the fault features extracted by this method can well reflect the fault status information of the circuit, and the fault diagnosis accuracy of the proposed method reaches 99%. |
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