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To address the complex operating conditions and diverse fault types of electric mining truck drive motors, a fault diagnosis method integrating the Caterpillar Fungus Optimizer (CFO) with Support Vector Machine (SVM) is proposed. A parameter optimization model is constructed to jointly optimize the slack variable, penalty factor, and kernel function parameter of SVM, thereby improving fault recognition accuracy and model stability. Fault sample data are collected under typical operating conditions for identification experiments, and uniformly distributed random disturbances with different amplitudes are introduced to evaluate model robustness. Comparative analysis with several commonly used optimization methods demonstrates that the proposed approach exhibits strong adaptability in terms of fault classification accuracy, anti-interference capability, and model training stability. The average recognition accuracy reaches 98.67%, while the accuracy remains above 94.7% under disturbance conditions.

, authors=Jiajun Zhang, authorsList=Jiajun Zhang, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309640001135141659, articleId=1309639999541306137, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于CFO-SVM的电动矿卡驱动电机故障诊断方法研究, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对电动矿卡驱动电机运行状态复杂、故障类型多样的问题，提出一种融合虫草优化算法（CFO）与支持向量机（SVM）的故障诊断方法。通过构建参数寻优模型，对支持向量机中的松弛变量、惩罚因子与核函数参数进行联合优化，以提升故障识别的准确性和模型的稳定性。在典型运行工况下采集故障样本数据，进行识别试验，并引入不同幅度的均匀随机扰动进行鲁棒性测试。结合多种常见优化方法对比分析模型的性能表现，结果显示该方法在故障分类准确率、抗干扰能力和模型训练稳定性方面具有较强适应性，平均识别准确率达98.67%，在扰动条件下准确率保持在94.7%以上。

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