华中科技大学 a. 船舶与海洋工程学院； b. 高新船舶与深海开发装备协同创新中心；c. 制造装备数字化国家工程中心， 武汉 430074
出版日期:2018-05-28发布日期:2018-05-28基金资助:国家自然科学基金项目(51475189)，国际科技合作内地与澳门联合项目(S2016G1012)，中央高校基本科研业务费专项资金项目(2016YXMS050)，甲板机械质量品牌专项经费资助项目Data-Driven Performance Degradation Condition Monitoring for Rolling Bearings
WU Jun a,b,LI Guoqiang a,WU Chaoyong a,CHENG Yiwei c,DENG Chao ca. School of Naval Architecture and Ocean Engineering; b. Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration; c. National Engineering Research Center of Digital Manufacturing Equipment, Huazhong University of Science and Technology, Wuhan 430074, China
Online:2018-05-28Published:2018-05-28摘要/Abstract
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.
关键词: 滚动轴承, 经验模态分解, 主成分分析, 状态监测
Abstract: A degradation condition monitoring method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and principal component analysis (PCA) for rolling bearings is proposed, which considers the problems of weak fault signals and diverse fault modes. In this paper, intrinsic energy features and failure frequency features are respectively extracted from the preprocessed original vibration signals of rolling bearings using CEEMDAN algorithm and Hilbert-Huang transform. Then, a health index of the rolling bearings is obtained through the feature fusion using spearman rank correlation coefficient and PCA. Finally, the degradation condition of the rolling bearings is identified by the analysis of the monotonicity, robustness and correlation of the health index. The conclusion is drawn by a case study that the proposed method can be applied to accurately and timely identifying the degradation condition of the rolling bearings.
Key words: rolling bearing, empirical mode decomposition, principal component analysis (PCA), condition monitoring
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