摘要
针对传统双谱难以有效处理强噪声干扰以及相关熵运算量大的问题,提出了一种基于不完全Cholesky分解相关熵和双谱分析的轴承故障诊断方法。该方法在不求出核矩阵的情况下,首先利用不完全Cholesky分解算法和核函数,计算核矩阵的低秩分解下三角矩阵;其次,利用Gini指数选取下三角矩阵的主分量,利用下三角矩阵的主分量计算核矩阵的低秩近似矩阵,进而计算信号的相关熵;最后,计算振动信号相关熵的双谱,根据相关熵的双谱特征识别轴承故障。通过不完全Cholesky分解算法和Gini指数计算信号的相关熵,不仅压缩了数据量,突出了轴承故障瞬态冲击特征,有效抑制了噪声的影响,而且提高了计算效率,减少了计算机内存占用量。通过仿真和试验轴承故障振动信号分析结果表明:强背景噪声会造成传统双谱故障诊断方法失效,而基于相关熵和双谱分析的轴承故障诊断方法,能在强噪声干扰背景中提取轴承故障瞬态冲击特征,准确识别轴承故障,其性能优于传统双谱和小波变换域双谱,为一种轴承故障诊断的有效方法。
Aiming at the problem of traditional bi-spectrum being difficult to effectively deal with strong noise interference and large calculation amount of correntropy,a bearing fault diagnosis method based on incomplete Cholesky decomposition correntropy and bi-spectrum analysis was proposed.Under the case of not solving kernel matrix,firstly,incomplete Cholesky decomposition algorithm and kernel function were used to calculate the lower triangular matrix of low rank decomposition of kernel matrix.Secondly,principal components of the lower triangular matrix were selected using Gini index,and principal components of the lower triangular matrix were used to calculate the low rank approximate matrix of kernel matrix and then the correntropy of bearing vibration signal was calculated.Finally,the bi-spectrum of correntropy of the signal was calculated,and bearing faults were identified using bi-spectrum characteristics of correntropy.It was shown that using incomplete Cholesky decomposition algorithm and Gini index to calculate correntropy of signal can not only compress the amount of data,highlight transient impact characteristics of bearing faults,and effectively suppress effects of noise,but also improve calculation efficiency and reduce occupation of computer memory.Simulated and actually measured bearing fault vibration signals’analysis showed that strong background noise can cause failure of traditional bi-spectrum fault diagnosis method,while the proposed method based on correntropy and bi-spectrum analysis can extract transient impact characteristics of bearing faults under strong background noise interference and accurately identify bearing faults.The proposed method’s performance is better than those of traditional bi-spectrum and wavelet transform domain bi-spectrum,it is an effective method for bearing fault diagnosis.
作者
李辉
郝如江
LI Hui;HAO Rujiang(School of Mechanical Engineering,Tianjin University of Technology and Education,Tianjin 300222,China;School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2022年第11期123-132,共10页
Journal of Vibration and Shock
基金
国家自然科学基金项目(51375319)
河北省自然科学基金项目(E2013421005)。
