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一种基于EEMD-PSO-SVM的模拟电路故障诊断方法

Shuhan Zhao¹, Xu Liang¹, Ling Wang¹

¹College of Mechanical & Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.

|September 30, 2024

概括

本研究介绍了一种先进的模拟电路故障诊断方法,使用集体实证模式分解 (EEMD),最大信息系数 (MIC) 和带有支持向量机 (SVM) 的粒子群优化 (PSO). 这种新的方法显著提高了复杂电子系统的诊断准确性和效率.

科学领域:

电气工程电气工程
信号处理信号处理
机器学习机器学习

背景情况:

模拟电路对于电子设备的可靠性和安全性至关重要.
传统的故障诊断方法与非线性,非静止信号和参数选择作斗争.
低诊断准确度和模型复杂性阻碍了有效的模拟电路故障检测.

研究的目的:

为模拟电路提出一种新的故障诊断方法.
解决传统方法在准确性和参数选择方面的局限性.
通过改进故障检测,提高电子设备的可靠性和安全性.

主要方法:

集体实证模式分解 (EEMD) 用于从故障信号中进行自适应的多尺度特征提取.
对于初始特征向量构造的皮尔森相关系数和能量值分析.
最大信息系数 (MIC) 算法用于优化特征选择.
粒子集群优化 (PSO) 调整支持矢量机 (SVM) 的超参数进行分类.

主要成果:

拟议的方法有效地使用EEMD提取多个规模的故障特征.
MIC算法成功优化了特征向量,减少了复杂性.
优化了PSO的SVM实现了卓越的分类准确性和模型训练效率.
该方法克服了与波段基础函数选择相关的挑战.

结论:

集成的EMD-MIC-PSO-SVM方法为模拟电路故障诊断提供了一个强大的解决方案.
与传统技术相比,这种方法显著提高了诊断的准确性和效率.
拟议的方法有助于提高电子系统的安全性和可靠性.

关键词:

在 EEMD 中,我们可以使用 EEMD.错误诊断是一个错误的诊断.功能选择功能选择在MIC中,MIC是MIC,MIC是MIC.公共服务机构-SVM

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