上海交通大学学报(自然版) ›› 2018, Vol. 52 ›› Issue (5): 538-544.doi: 10.16183/j.cnki.jsjtu.2018.05.006
吴军a, b,黎国强a,吴超勇a,程一伟c,邓超c
出版日期:
2018-05-28
发布日期:
2018-05-28
基金资助:
WU Jun a,b,LI Guoqiang a,WU Chaoyong a,CHENG Yiwei c,DENG Chao c
Online:
2018-05-28
Published:
2018-05-28
摘要: 针对滚动轴承性能衰退状态监测中的故障信号微弱和故障模式多样性等问题,提出了一种基于自适应噪声完备集合经验模态分解(CEEMDAN)以及主成分分析(PCA)的滚动轴承性能衰退状态监测方法.针对经过预处理后的滚动轴承原始振动信号,分别采用CEEMDAN方法以及Hilbert-Huang变换提取本征模能量特征和故障特征;综合运用斯皮尔曼等级相关系数和PCA进行特征融合,以获得表征滚动轴承性能衰退状态的健康指数;通过对健康指数的单调性、稳健性和衰退性等进行分析,并经过加权平均来识别滚动轴承性能衰退状态.实例分析结果表明,所提出的方法能够较为准确地识别滚动轴承性能衰退状态.
中图分类号:
吴军a, b,黎国强a,吴超勇a,程一伟c,邓超c. 数据驱动的滚动轴承性能衰退状态监测方法[J]. 上海交通大学学报(自然版), 2018, 52(5): 538-544.
WU Jun a,b,LI Guoqiang a,WU Chaoyong a,CHENG Yiwei c,DENG Chao c. Data-Driven Performance Degradation Condition Monitoring for Rolling Bearings[J]. Journal of Shanghai Jiaotong University, 2018, 52(5): 538-544.
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