摘要
针对滚动轴承早期故障易受噪声影响难以准确提取特征信息的问题,提出了一种基于最大相关峭度解卷积(MCKD)和变分模态分解(VMD)关联维数的故障诊断AR模型。采用MCKD对滚动轴承振动信号进行降噪处理,滤除噪声影响;对降噪后的信号进行VMD分解,选择对故障特征敏感的IMF分量进行信号重构,并对重构信号建立AR模型,获取自回归参数;计算在指定嵌入维数上自回归参数的关联维数,对滚动轴承的故障进行诊断。实验结果表明,所提方法能够有效提取故障信号中的特征信息,证明了方法的有效性。
Aiming at the problem that early faults of rolling bearings are easily affected by noise and it is difficult to accurately extract featureinformation,a fault diagnosis AR model based on the correlation dimension of maximum correlation kurtosis deconvolution(MCKD) andvariational modal decomposition(VMD) was proposed.MCKD was used to de-noise the rolling bearing vibration signal to filter out theinfluence of noise;VMD decomposition was performed on the de-noised signal,the IMF component sensitive to fault characteristics wasselected for signal reconstruction,and AR for the reconstructed signal model was built,the autoregressive parameters were obtained.Thecorrelation dimension of the autoregressive parameters on the specified embedding dimension was calculated to diagnose the fault of the rollingbearing.The experimental results show that the proposed method can effectively extract the characteristic information in the fault signal,whichproves the effectiveness of the method.
作者
申童
刘复秋宣
高学亮
岳晓峰
Shen Tong;Liu Fuqiuxuan;Gao Xueliang;Yue Xiaofeng(School of Mechanical and Electrical Engineering,Changchun University of Technology,Changchun 130012,China)
出处
《机电工程技术》
2021年第6期18-21,125,共5页
Mechanical & Electrical Engineering Technology
基金
国家自然科学基金项目(编号:51505038)
吉林省教育厅“十三五”科学技术项目(编号:JJKH20200654KJ)。
作者简介
申童(1992-),男,山东日照人,硕士研究生,研究领域为机器视觉与智能检测;岳晓峰(1971-),男,吉林长春人,博士,教授,研究领域为机器视觉与智能检测,已发表论文23篇。
