摘要
提出了一种基于特征波形稀疏匹配的滚动轴承故障模式识别方法.该方法通过自行设计的搜索算法从信号中提取多段特征波形,并对其进行学习优化,以优化后的特征波形作为基原子模型生成原子库及模式匹配库.将待识别信号在模式匹配库上进行一阶匹配分析,实现轴承故障的模式识别.对正常轴承、滚动体故障、内圈故障和外圈故障信号进行实验,验证了方法的有效性和鲁棒性.
A method of fault pattern recognition for rolling bearings was proposed on the basis of sparse matching of a characteristic waveform （CW）.With a well-designed search algorithm,multi-section CWs were extracted from a vibration signal.A representative CW was obtained by learning from the extracted CWs.Then,the representative CW was acted as an atom model to construct a dictionary and a pattern matching dictionary.Pattern recognition was conducted through one-order matching analysis in the pattern matching dictionary.Employing the signals of a normal bearing,ball fault,inner race fault and outer race fault for pattern recognition,the result indicates that the method is valid and robust.
出处
《北京科技大学学报》
EI
CAS
CSCD
北大核心
2010年第3期390-396,共7页
Journal of University of Science and Technology Beijing
基金
国家自然科学基金资助项目(No.50705069
No.50674010
9)
高等学校博士学科点专项科研基金资助项目(No.20No.50905013
No.50934007)
国家高技术研究发展计划资助项目(No.2007AA04Z16070008050
No.20090006120007)
关键词
滚动轴承
点蚀
模式识别
特征波形
rolling bearings
pitting
pattern recognition
characteristic waveform
