摘要
解卷积方法已广泛应用于振动信号的故障冲击提取。然而设备运行工况复杂多变、故障特征周期难以准确预知以及随机冲击干扰,使得当前的解卷积方法难以适应工业现场复杂环境下故障冲击增强的需求。针对该问题,提出了一种基于信号子空间的新型盲解卷积方法。该方法通过奇异值分解(SVD)方法将测试信号空间分解,分离各子空间,在此基础上通过稀疏编码收缩抑制子空间噪声,以脉冲稀疏指数为指标筛选有效子空间,最后迭代实现故障脉冲提取。轴承变转速仿真试验和列车轴承试验结果表明,该方法不仅可以有效消除随机冲击和噪声,避免能量对子空间筛选的影响,而且在缺乏准确的故障特征周期情况下仍能实现故障脉冲的准确提取。
Deconvolution method is widely used in fault shock extraction of vibration signals.However,due to complex and changeable operating conditions of equipment,fault feature period is difficult to accurately predict,and random impact interferences make the current deconvolution method be difficult to meet needs of fault impact enhancement in complex environment of industry site.Here,aiming at the above problems,a new blind deconvolution method based on signal subspace was proposed.With this method,testing signal space was decomposed with the singular value decomposition(SVD)method,and was separated into subspaces.Then,subspace noise was suppressed with sparse coding shrinkage,and the effective subspace was selected with the pulse sparsity index.Finally,fault pulses were extracted through iteration.The results of bearing varying rotating speed simulation tests and train bearing tests showed that the proposed method can not only effectively eliminate random impact and noise,and avoid effects of energy on subspace screening,but also realize accurate extraction of fault pulses in absence of correct fault feature period information.
作者
周涛
赵明
郭栋
欧曙东
ZHOU Tao;ZHAO Ming;GUO Dong;OU Shudong(State Key Laboratory for Manufacturing System Engineering,Xi’an Jiaotong University,Xi’an 710049,China;Key Laboratory of Advanced Manufacture Technology for Automobile Parts,Ministry of Education,Chongqing University of Technology,Chongqing 400054,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2022年第3期139-147,共9页
Journal of Vibration and Shock
基金
国家自然科学基金面上项目(51875434)
汽车零部件先进制造技术教育部重点实验室开放课题(2019KLMT02)。
关键词
盲解卷积
奇异值分解(SVD)
最小熵解卷积
变转速
blind deconvolution
singular value decomposition(SVD)
minimum entropy deconvolution
varying rotating speed
