摘要
当柔性薄壁轴承工作时,受长短轴交替产生的冲击成分以及背景噪声的影响,很难从振动信号频谱中提取出故障频率。针对这问题,提出奇异值分解(SVD)与多点最优调整的最小熵解卷积(MOMEDA)相结合的柔性薄壁轴承故障特征提取方法。该方法用SVD算法对原始信号作降噪处理,获得重构信号,应用MOMEDA对重构信号进行增强,突出周期性故障脉冲,通过对处理后的信号进行频谱分析,从而提取出相应的故障频率。通过频谱中的主导频率与柔性薄壁轴承的故障特征频率的对比,可以判断故障位置,实现轴承的故障诊断。试验数据分析结果表明,该方法可以有效提取轴承内、外圈的故障频率。
When the flexible thin-walled bearing works,it is difficult to extract the fault frequency from the spectrum of vibration signal because of the influence of the impact component alternately generated by the long and short axes and the influence of background noise.Aimed at this problem,a fault feature extraction method for flexible thin-walled bearings combined with singular value decomposition(SVD)and multi-point optimal adjustment minimum entropy deconvolution(MOMEDA)was proposed.In this method,the original signal is denoised by SVD algorithm to obtain the reconstructed signal,the reconstructed signal is enhanced by MOMEDA to highlight the periodic fault pulse,and the processed signal is analyzed by spectrum to extract the corresponding fault frequency.By comparing the dominant frequency in the spectrum with the fault characteristic frequency of the flexible thin-walled bearing,the fault location can be judged and the fault diagnosis of the bearing can be realized.The test data analysis results show that the method can effectively extract the fault frequency of the inner and outer rings of the bearing.
作者
郑嘉伟
刘其洪
李伟光
严嵩
ZHENG Jiawei;LIU Qihong;LI Weiguang;YAN Song(School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou Guangdong 510640,China)
出处
《润滑与密封》
CAS
CSCD
北大核心
2020年第12期91-96,共6页
Lubrication Engineering
基金
国家高技术研究发展计划(863计划)项目(2015AA043005)
国家自然科学基金项目(51875205,51875216)
广东省自然科学基金项目(2018A030310017,2019A1515011780)
广东省教育厅项目(2018KQNCX191)
广州市科技计划项目(201904010133)
广东省重大科技专项(2019B090918003).
关键词
柔性薄壁轴承
奇异值分解
多点最优调整的最小熵解卷积
故障特征提取
flexible thin-walled bearing
singular value decomposition
minimum entropy deconvolution of multi-point optimal adjustment
fault feature extraction
