摘要
滚动轴承作为旋转机械设备的重要部件之一,其工作状态直接影响旋转设备的运行安全,因此其故障特征的有效提取对于保障机械设备正常运行具有重要的意义。实际应用中滚动轴承通常以变化的速度运行,并且单一传感器采集的轴承的非平稳信号往往被严重的背景噪声覆盖,使得故障特征的提取非常困难。为了解决这一问题,提出一种变转速下L_(1,1,2)范数与张量核范数联合约束的张量主成分分析(tensor robust principal component analysis,TRPCA)滚动轴承故障特征提取方法。首先,使用时频表示(time-frequency representation,TFR)作为正向切片构建张量,分别探讨滚动轴承时变故障特征在张量域中的管稀疏性和背景噪声在张量域中的低管秩性。进而使用L_(1,1,2)范数与张量核范数联合约束的TRPCA对故障特征张量进行提取,得到管稀疏的故障特征张量。最后将提取的故障特征张量在通道索引中进行融合,得到能够有效表征故障特征的时频表示。仿真和试验分析验证了该方法在轴承故障特征提取中的有效性。
As one of important components of rotating mechanical equipment,rolling bearing's working status directly affects the operational safety of rotating equipment.Therefore,its fault feature effective extraction is of great significance for ensuring normal operation of mechanical equipment.In practical applications,rolling bearings usually operate at varying rotating speed,and non-stationary signals collected by a single sensor are often covered by severe background noise to make it very difficult to extract fault features.Here,to solve this problem,a tensor robust principal component analysis(TRPCA)rolling bearing fault feature extraction method with joint constraints of L_(1,1,2) norm and tensor kernel norm under variable rotating speed was proposed.Firstly,the time frequency representation(TFR)was taken as a forward slice to construct tensors and explore the tube sparsity of time-varying fault features of rolling bearing in tensor domain and the low tube rank property of background noise in tensor domain.Then,the TRPCA with joint constraints of L_(1,1,2) norm and tensor kernel norm was used to extract fault feature tensors and obtain fault feature tensors with tube sparsity.Finally,the extracted fault feature tensors were fused in channel index to obtain a TFR which could effectively represent fault features.Simulation and experimental results verified the effectiveness of the proposed method in bearing fault feature extraction.
作者
王冉
曹徐
张军武
余亮
WANG Ran;CAO Xu;ZHANG Junwu;YU Liang(School of Logistics Engineering,Shanghai Maritime University,Shanghai 201306,China;State Key Lab of Mechanical System and Vibration,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2024年第7期84-93,共10页
Journal of Vibration and Shock
基金
国家自然科学基金项目(51505277,12074254)
上海市自然科学基金资助项目(21ZR1434100)
机械系统与振动国家重点实验室自主课题资助(MSVZD202201)。
关键词
张量
故障特征提取
变转速工况
张量主成分分析(TRPCA)
管稀疏
tensor
fault feature extraction
variable rotating speed working condition
tensor robust principal component analysis(TRPCA)
tube sparsity
