计量学报2017，Vol.38Issue(4)：449-452,4.DOI:10.3969/j.issn.1000-1158.2017.04.14
基于DEMD局部时频熵和SVM的风电齿轮箱故障诊断方法研究
Wind Power Gear Box Fault Diagnosis Based onDEMD Local Frequency Entropy and SVM
摘要
Abstract
In order to extract the constituent of useful information from the nonlinear and non-stationary wind gearbox fault signal.A new approach for wind power gear box fault diagnosis based on the combination of differential-based empirical mode decomposition(DEMD), local frequency entropy and support vector machine(SVM) is proposed.Firstly, fault vibration signal is filtered with adaptive multi-scale mathematical morphology.Then mechanical vibration signal is decomposed with DEMD to obtain a certain number of intrinsic mode functions(IMF).Then the local time-frequency entropy of the IMF components are calculated and used as the eigenvectors of SVM.Finally, the eigenvectors are put into SVM to identify the state of the wind power gear box.The experimental results show that the method based on the combination of DEMD, local time-frequency entropy and SVM can be used to recognize and classify rolling bearing fault signals accurately and effectively.关键词
计量学/故障诊断/风电齿轮箱/微分经验模式分解/形态滤波/支持向量机/局部时频熵Key words
metrology/fault diagnosis/wind power gear box/DEMD/SVM/local time-frequency entropy分类
通用工业技术引用本文复制引用
孟宗,刘东,岳建辉,詹旭阳,马钊,李晶..基于DEMD局部时频熵和SVM的风电齿轮箱故障诊断方法研究[J].计量学报,2017,38(4):449-452,4.基金项目
国家自然科学基金 (51575472,61673334) （51575472,61673334）
河北省自然科学基金 (E2015203356) （E2015203356）
河北省高等学校科学研究计划重点项目(ZD2015049) （ZD2015049）
