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To enhance the accuracy and stability of fault prediction for mining excavator engines under high-intensity and complex working conditions, a fault prediction model based on the Grey Wolf Optimizer (GWO) optimized Support Vector Machine (SVM) was constructed. Key operating parameters of the Weichai WP14T mining excavator engine were collected during real-time operation. Data preprocessing was conducted using outlier detection and elimination as well as data standardization. Feature selection was performed using the Pearson correlation coefficient method to construct a sample set with a reasonable structure and complete feature information. The Grey Wolf Optimizer was employed to adaptively and globally optimize the penalty factor and kernel function parameters of the Support Vector Machine, thereby enhancing the model's nonlinear learning and generalization capabilities. Fault prediction experiments were designed, and comparative verification was carried out under various typical fault conditions. The results show that the GWO-SVM model exhibits higher accuracy and stronger robustness in multi-class fault prediction. The prediction accuracy on the test set reaches 92.8%, which is 9.9 percentage points higher than that of the traditional SVM, and it maintains stable output under conditions of few-sample interference and data fluctuation.

, authors=Jiajun Zhang, Jie Chen, Ming Ma, authorsList=Jiajun Zhang, Jie Chen, Ming Ma, 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=1321893443316601164, articleId=1321893441504661834, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于GWO-SVM的矿用挖掘机发动机故障预测方法研究, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

为提升矿用挖掘机发动机在高强度复杂工况下的故障预测准确性与稳定性，构建了基于灰狼优化算法（GWO）优化支持向量机（SVM）的故障预测模型。通过采集潍柴WP14T型矿用挖掘机发动机实时运行过程中的关键工况参数，使用异常值检测与剔除、数据标准化等手段进行数据预处理，并采用皮尔逊相关系数法进行特征选择，构建结构合理、特征信息完备的样本集。利用灰狼优化算法对支持向量机中的惩罚因子与核函数参数进行自适应全局寻优，以提升模型的非线性学习和泛化能力。设计故障预测试验，在多种典型故障工况下开展对比验证，结果表明：GWO-SVM模型在多类故障预测中表现出更高的准确性和更强的鲁棒性，在测试集上的预测准确率高达92.8%，较传统SVM提高了9.9个百分点，在少样本干扰及数据波动条件下仍能保持稳定输出。

, authors=张家俊, 陈杰, 马明, authorsList=张家俊, 陈杰, 马明, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=+wFd5SvHUv6IuwEX0hQ3VA==, magXml=qeGSjVzgfgL7Bud51+u47A==, pdfUrl=null, pdf=vN2aej3dK9INwzMWn/hKYg==, pdfFileSize=3532809, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=5bxEZPDd+Pw3HKTZlvgswA==, mapNumber=null, fund=null)}, authors=[Author(id=1321893443656339793, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893441504661834, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=zhangjiajun1@ntou.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893443727642963, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893441504661834, authorId=1321893443656339793, language=EN, stringName=Jiajun Zhang, firstName=Jiajun, middleName=null, lastName=Zhang, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=School of Mechanical and Electrical Engineering, The City Vocational College of Jiangsu (Nantong Campus), Nantong, Jiangsu 226001, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893443798946132, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893441504661834, authorId=1321893443656339793, language=CN, stringName=张家俊, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=江苏城市职业学院南通办学点 机电工程学院，江苏 南通市　226001, bio={"content":"

张家俊（1989—），男，山东临沂人，硕士，讲师，主要从事发动机故障诊断方面的研究与教学工作。E-mail:zhangjiajun1@ntou.edu.cn

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张家俊（1989—），男，山东临沂人，硕士，讲师，主要从事发动机故障诊断方面的研究与教学工作。E-mail:zhangjiajun1@ntou.edu.cn

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