《计算机应用研究》|Application Research of Computers

基于改进EMD的滚动轴承故障增长特征提取和损伤评估技术

Fault growth feature extraction and damage assessment for rolling bearing based on improved EMD

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作者 常竞,温翔
机构 1.四川财经职业学院,成都 610101;2.四川省计算机研究院,成都 610041
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文章编号 1001-3695(2019)05-040-1474-04
DOI 10.19734/j.issn.1001-3695.2017.11.0778
摘要 针对传统滚动轴承损伤评估方法未考虑故障特征的稳定性和有效性导致评估的准确度不高的问题,提出了基于改进EMD(empirical mode decomposition)的滚动轴承故障增长特征提取和损伤评估技术。使用EMD将不同损伤程度的故障信号分解为一系列的奇异值分量(singularity value decomposition,SVD),建立不同SVD分量描述的故障增长趋势,分析各级分量对故障增长过程的稳定性和敏感性,提取能够有效感知故障增长过程的奇异值分量作为故障增长特征,建立滚动体、内环和外环故障以及不同故障严重程度下的样本模型,利用智能算法辨识故障类型并评估其严重程度。最后,使用凯斯西储大学公开的滚动轴承振动数据验证所提方法的有效性。实验结果证明,故障增长分析方法能够从复杂的奇异值分量中筛选出有效跟踪故障增长过程的特征,对提高损伤评估的准确度具有重要意义。
关键词 滚动轴承; 损伤评估; 经验模态分解; 奇异值分量
基金项目 四川省科技厅重点研发项目(2016GZ0427)
本文URL http://www.arocmag.com/article/01-2019-05-040.html
英文标题 Fault growth feature extraction and damage assessment for rolling bearing based on improved EMD
作者英文名 Chang Jing, Wen Xiang
机构英文名 1.Sichuan Vocational College of Finance & Economics,Chengdu 610101,China;2.Sichuan Institute of Computer Sciences,Chengdu 610041,China
英文摘要 Traditional damage assessment methods for rolling bearings didn't consider sensitivity and stability of fault features to track fault growth process, it resulted in low precision and accuracy. This paper proposed a new feature extraction and damage assessment method for rolling bearings based on improved empirical mode decomposition(EMD) and singularity value decomposition(SVD). For the different damage severity of each fault, it decomposed original vibration signals into a finite number of intrinsic mode functions(IMF), and calculated their singular value sequences, thus built SVD components related to fault growth trends. Through analyzing the sensitivity and stability for fault growth trends described by SVD components, it selected the optimal SVD sequences which could track fault growth process as faulty eigenvectors of fault type recognition and damage assessment. Finally, it verified the effectiveness of proposed methods by using the open vibration data of rolling bearing provided by Case Western Reserve University. The results show that the fault growth features can be selected from complex SVD components by comparing their abilities of monitoring and tracking fault growth process, and the proposed approach can provide a better strategy for fault feature extraction of rolling bearings in order to improve damage assessment precision and accuracy.
英文关键词 rolling bearing; damage assessment; empirical mode decomposition; singularity value decomposition
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收稿日期 2017/11/6
修回日期 2017/12/29
页码 1474-1477,1481
中图分类号 TP277
文献标志码 A