多特征提取与IGWO-SVM的谐波减速器故障识别  

Multi-feature extraction and IGWO-SVM for harmonic reducer fault diagnosis

作　　者：刘彪 石超 郭世杰[1,2] LIU Biao;SHI Chao;GUO Shijie(School of Mechanical Engineering,Inner Mongolia University of Technology,Hohhot 010051,CHN;Key Laboratory of Inner Mongolia Autonomous Region Special Service Intelligent Robot,Hohhot 010051,CHN)

机构地区：[1]内蒙古工业大学机械工程学院,内蒙古呼和浩特010051 [2]内蒙古自治区特殊服役智能机器人重点实验室,内蒙古呼和浩特010051

出　　处：《制造技术与机床》2024年第10期5-12,共8页Manufacturing Technology & Machine Tool

基　　金：国家自然科学基金资助项目(52065053,52365064);内蒙古关键技术攻关资助项目(2020GG0255);内蒙古自治区高等学校青年科技英才支持计划资助项目(NJYT23043)。

摘　　要：为解决对谐波减速器进行故障诊断时,提取的特征信息不足、使用的分类网络容易陷入过拟合的问题,提出了利用改进的完全自适应噪声集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)降噪与多特征提取结合改进灰狼优化算法(improved grey wolf optimizer,IGWO)优化支持向量机(support vector machine,SVM)的故障识别方法。首先,对采集到的不同故障源的多工况谐波减速器振动信号进行ICEEMDAN分解,应用相关性分析完成信号重构,实现信号降噪处理;其次,提取数据的时频熵特征,丰富所提取数据的特征信息;最后,通过对GWO的收敛因子、比例权重和种群初始化进行改进,构建IGWO-SVM对数据进行分类,完成谐波减速器故障识别。结果表明,所提方法的平均准确率可以达到91.27%,相较于GWO-SVM验证集准确率由87.5%提升到了90%,所提方法能够有效地对多工况谐波减速器进行故障识别,且具有较强的泛化能力。To solve the problem that the extracted feature information was insufficient and the classification network was easy used to fall into over-fitting when the fault diagnosis of harmonic reducer was carried out.A fault identification method was proposed,which combined improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)for denoising and multi-feature extraction,and improved grey wolf optimization(IGWO)to optimize support vector machine(SVM).Firstly,the vibration signals of multi-condition harmonic reducer with different fault sources were decomposed by ICEEMDAN,and correlation analysis was applied to complete signal reconstruction and realized signal noise reduction processing.Secondly,the time-frequency entropy feature of the data was extracted to reduce the dimension of the data and enrich the feature information of the data.Finally,by improving the convergence factor,proportional weight and population initialization of GWO,the IGWO-SVM model was constructed to classify the data and had completed the fault identification of harmonic reducer.The results show that the average accuracy of the proposed method can reach 91.27%.Compared with the verification set of GMO-SVM,the accuracy of the proposed method is improved from 87.5%to 90%.The proposed method can effectively identify the fault of the multioperating harmonic reducer,and has strong generalization ability.

关 键 词：故障识别 谐波减速器 时频熵特征 支持向量机 改进灰狼算法 

分 类 号：TH165.3[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]