摘要
滚动轴承运行时,其强烈的振动噪声通常会与轴承早期微弱故障叠加,导致其瞬态故障特征难以提取,因此提出了一种轴承早期微弱故障信号瞬态特征的时频分析方法。首先,通过自适应噪声集合模态分解对数据做预处理,使用峭度筛选出了有效模态分量,并进行了重构降噪;然后,对重构信号做了瞬态提取变换,并进行了瞬态特征提取;最后,利用提取到的瞬态信号进行了故障诊断;对仿真信号和实验信号进行了处理,并将其与其他常用时频分析方法进行了比较。研究结果表明:该方法可以有效地提取滚动轴承故障瞬态特征,提高复杂环境下滚动轴承故障早期信号的噪声鲁棒性;同时,时频能量特征更集中,可以清楚地看到瞬态信号的间隔,并能有效表征信号的早期故障特征频率。
Aiming at the problem that the strong vibration and noise of rolling bearings were usually superimposed with the early weak faults of the bearing,which made it difficult to extract the transient fault characteristics,a time-frequency analysis method was proposed to analyze the transient characteristics of the early weak fault signals of the bearing.Firstly,the data was preprocessed by complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),the effective modal components were filtered out using kurtosis and were reconstructed to reduce noise.Then,the reconstructed signal was subjected to transient extraction transform(TET)for transient feature extraction.Finally,the fault diagnosis was performed using the extracted transient signals.The simulation signal and experimental signal were processed and compared with other common time-frequency analysis methods.The results show that this method can effectively extract the fault transient characteristics,improve the noise robustness of early fault signals of rolling bearing under complex environment,focus the time-frequency energy characteristics more clearly,clearly see the interval of transient signals,and can effectively represent the early fault characteristic frequency of the signal.
作者
陈志刚
赵杰
张楠
车昊阳
CHEN Zhi-gang;ZHAO Jie;ZHANG Nan;CHE Hao-yang(School of Mechatronics and Vehicle Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Construction Safety Monitoring Engineering Technology Research Center of Beijing,Beijing 100044,China;Changqing Downhole Technology Company,CNPC.Chuanqing Engineering Company Limited,Xi an 710021,China)
出处
《机电工程》
CAS
北大核心
2021年第6期697-703,711,共8页
Journal of Mechanical & Electrical Engineering
基金
国家自然科学基金资助项目(51605022)
北京市属高校基本科研资助项目(X20061,X20071)
北京建筑大学研究生创新资助项目(PG2020091)。
关键词
滚动轴承
自适应噪声集合模态分解
时频分析
有效模态分量
瞬态提取变换
rolling bearing
complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)
time-frequency analysis(TFA)
effective modal components
transient-extracting transform(TET)
