结合DSHDD和模糊评价的滚动轴承退化状态在线识别  

Online identification of rolling bearing degradation state based on DSHDD and fuzzy evaluation

作　　者：周建民 尹文豪 游涛 张龙 王发令[1,2] 余加昌 ZHOU Jian-min;YIN Wen-hao;YOU Tao;ZHANG Long;WANG Fa-ling;YU Jia-chang(School of Mechatronics and Vehicle Engineering,East China Jiaotong University,Nanchang 330013,China;Key Laborato-ry of Conveyance and Equipment of Ministry of Education,Nanchang 330013,China)

机构地区：[1]华东交通大学机电与车辆工程学院,江西南昌330013 [2]载运工具与装备教育部重点实验室,江西南昌330013

出　　处：《振动工程学报》2021年第3期646-653,共8页Journal of Vibration Engineering

基　　金：国家自然科学基金资助项目(51865010,51665013)。

摘　　要：提出一种用自适应噪声辅助的集合经验模态分解(CEEMDAN)和能量熵结合提取振动信号的特征的方法,将特征输入到双超球数据域描述(DSHDD)模型中,再将得到的结果输入到隶属度函数中,计算隶属度,以此作为性能退化评估的指标。使用3σ设置自适应阈值,确定轴承早期失效阈值。用CEEMDAN和Hilbert包络解调的方法验证评估结果的正确性。最后利用美国辛辛那提大学的轴承全寿命周期数据验证该模型的有效性和实用性。In the long-term use process,the performance of rolling bearing will be degraded to different degrees.If the degradation state of rolling bearing can be identified online,accidents can be effectively prevented.In this paper,an adaptive noise-assisted collective empirical mode decomposition(CEEMDAN)method combined with energy entropy is proposed to extract the characteristics of vibration signals,and then the characteristics are input into the DSHDD model,and the obtained results are input into the membership function to calculate the membership,which can be used as the evaluation index of performance degradation.An adaptive threshold is set using 3σto determine the bearing’s early failure threshold.CEEMDAN and Hilbert envelope demodulation methods are used to verify the correctness of the evaluation results.The validity and practicability of the model are verified by using the bearing life cycle data from the University of Cincinnati.

关 键 词：故障诊断 滚动轴承 集合经验模态分解 双超球数据域描述 性能退化评估 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH133.33