可再生能源2016，Vol.34Issue(10)：1481-1490,10.
基于EEMD峭度-相关系数准则的多特征量风电机组轴承故障诊断
Fault diagnosis method of wind turbine's bearing based on EEMD kurtosis-correlation coefficients criterion and multiple features
摘要
Abstract
According to the characteristics of high noise,nonlinear and non-stationary of bearing of wind turbine,a method of bearing fault diagnosis based on ensemble empirical mode decomposition (EEMD)and kurtosis-correlation coefficients criterion as well as multiple features was proposed in this paper.Firstly,the original vibration signals were processed to several IMFs by using ensemble empirical mode decomposition.A set of IMFs were selected by the kurtosis-correlation coefficients criterion which contained the most mounts of information.Secondly,a matrix of multiply features was extracted and constructed from the set of IMFs through three aspects which included time domain,singular value of the auto-regressive mode parameters matrix and energy entropy.In order to set up a predictive model of multiply classification,the matrix was put into the support vector machine.To optimized the kernel parameters and penalty parameters for attaining the best accuracy rating of prediction,the data of laboratory were given to verify the method.The fault degrees of wind turbine's bearing which comes from actually monitoring can be accurately classified and diagnosed by using this method,and the results show that it is a feasible fault diagnosis method of the wind turbine's bearing.关键词
集成经验模态分解/峭度-相关系数/风机轴承/支持向量机/故障诊断Key words
ensemble empirical mode decomposition/kurtosis-correlation coefficient/wind turbine bearing/SVM/fault diagnosis分类
能源科技引用本文复制引用
彭进,王维庆,王海云,唐新安..基于EEMD峭度-相关系数准则的多特征量风电机组轴承故障诊断[J].可再生能源,2016,34(10):1481-1490,10.基金项目
新疆维吾尔自治区重点实验室项目(2016D03021) （2016D03021）
国家自然科学基金项目(51267017). （51267017）
