摘要
为了提高支持向量机(SVM)在轴承故障诊断时的准确率和识别效率,提出了一种基于具有自适应白噪声的完整集成经验模态分解方法(CEEMDAN)、改进灰狼优化算法(IGWO)和支持向量机(SVM)相结合的故障诊断方法。首先用CEEMDAN与Shannon熵对振动信号消噪、分解,获得典型故障的敏感信号;其次,将粒子群算法(PSO)惯性权重w与粒子“飞行”速度v引入灰狼优化算法(GWO),得到IGWO,通过IGWO算法优化SVM得到诊断模型的最优参数,增强SVM的学习能力和泛化能力;最后,利用美国西储大学的轴承试验数据验证优化模型的有效性。结果表明,IGWO算法优化SVM的模型可以准确、高效地对轴承进行故障诊断;与GA、PSO、和GWO算法优化的SVM模型相比,该方法的故障诊断准确率和识别效率更高。
In order to improve the accuracy and recognition efficiency of support vector machine(SVM)in bearing fault diagnosis,a complete integrated empirical mode decomposition method(CEEMDAN)with adaptive white noise and improved grey wolf optimization algorithm(IGWO)are proposed.A fault diagnosis method combined with a support vector machine(SVM).Firstly,CEEMDAN and Shannon entropy are used to denoise and decompose the vibration signal to obtain the sensitive signal of typical fault.Secondly,the particle swarm optimization(PSO)inertia weight w and the particle"flight"velocity v are introduced into the grey wolf optimization algorithm(GWO).IGWO optimizes SVM by IGWO algorithm to obtain the optimal parameters of the diagnostic model,and enhances the learning ability and generalization ability of SVM.Finally,the effectiveness of the optimization model is verified by the bearing test data of the Western Reserve University.The results show that the IGWO algorithm optimizes the SVM model to diagnose the bearing accurately and efficiently.Compared with the SVM model optimized by GA,PSO and GWO algorithm,the fault diagnosis accuracy and recognition efficiency of the method are higher.
作者
黄海松
范青松
魏建安
黄东
HUANG Hai-song;FAN Qing-song;WEI Jian-an;HUANG Dong(Key laboratory of Advanced Manufacturing Technology,Ministry of Education,Guizhou University,Guiyang 550025,China)
出处
《组合机床与自动化加工技术》
北大核心
2020年第3期22-25,31,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
国家自然科学基金(51865004)
贵州省科技重大专项计划(黔科合重大专项[2018]3002号)
贵州省科技计划项目(黔科合平台人才[2018]5781)
贵州省教育厅项目(黔科合KY字[2018]037)
贵州省科技重大专项计划(黔科合重大专项[2017]3004)
贵州工业攻关重点项目(黔科合GZ字[2016]2332)。
关键词
支持向量机
参数优化
改进灰狼优化算法
故障诊断
support vector machine
parameter optimization
improved grey wolf optimization algorithm
fault diagnosis
作者简介
黄海松(1977—),女,贵州大方人,贵州大学教授,博士生导师,研究方向为制造物联与制造大数据,(E-mail)hshuang@gzu.edu.cn;通讯作者:范青松(1996—),男,湖北房县人,贵州大学硕士研究生,研究方向为故障诊断、数据挖掘和机器学习,(E-mail)qs_fan126@.com。
