摘要
为了提取齿轮裂纹故障的特征参数并识别不同裂纹深度齿轮的类型,以单级齿轮箱中的圆柱齿轮为实验对象,采集3种不同裂纹深度齿轮的振动信号。对采集到的信号进行时频域分析和EEMD分解,分别提取时域特征参数和EEMD能量特征参数,分析和构造齿轮裂纹故障特征向量,选用基于径向基核函数的支持向量机分类方法进行不同裂纹深度齿轮的识别。结果表明:结合时域特征参数和EEMD能量特征参数构造的齿轮裂纹故障特征向量能准确识别不同裂纹深度齿轮的类型。
In order to extract the characteristic parameters of gear crack fault and identify the types of gear with different crack depths,the vibration signals of gear with three different crack depths were collected from the cylindrical gear in the single-stage gear box.The collected signals were analyzed in time-frequency domain and decomposed in EEMD,the time-domain characteristic parameters and EEMD energy characteristic parameters were extracted,the fault characteristic vectors of gear cracks were analyzed and constructed,and the classification algorithm of support vector machine based on radial basis kernel function was used to identify gear with different crack depths.The results show that the fault eigenvectors of gear crack constructed by combining time-domain characteristic parameters and EEMD energy characteristic parameters can be used to accurately identify the types of gear with different crack depths.
作者
唐静
王二化
朱俊
李栋
TANG Jing;WANG Erhua;ZHU Jun;LI Dong(Changzhou Key Laboratory of High Technology,Changzhou College of Information Technology,Changzhou Jiangsu 213164,China)
出处
《机床与液压》
北大核心
2020年第20期161-166,共6页
Machine Tool & Hydraulics
基金
常州信息职业技术学院自然科学科研课题(CXZK201802Y)
常州高技术重点实验室项目(CM20183004)
常州信息职业技术学院青年基金项目(CXZK2016007),江苏省青蓝工程中青年学术带头人。
关键词
EEMD
特征提取
齿轮
裂纹故障
EEMD(Ensemble empirical mode decomposition)
Feature extraction
Gear
Crack fault
