μ-SVD降噪算法及其在齿轮故障诊断中的应用  

μ-SVD Based Denoising Method and Its Application to Gear Fault Diagnosis

作　　者：曾鸣[1] 杨宇[1] 郑近德[1] 程军圣[1] 

机构地区：[1]湖南大学汽车车身先进设计制造国家重点实验室,长沙410082

出　　处：《机械工程学报》2015年第3期95-103,共9页Journal of Mechanical Engineering

基　　金：国家自然科学基金(51175158;51375152);湖南省研究生科研创新项目资助(CX2014B146);湖南大学汽车车身先进设计制造国家重点实验室自主研究课题(60870002);中央高校基本科研业务费专项基金(531107040301)资助项目

摘　　要：为了提取机械设备被强背景噪声淹没的故障特征,采用一种具有通用意义的基于奇异值分解(Singular value decomposition,SVD)的子空间降噪算法对信号进行处理,即?-SVD降噪算法。传统的SVD降噪算法是?-SVD降噪算法中拉格朗日乘子??0时的一种特殊情况。?-SVD降噪算法包含滤值因子,能够抑制以噪声贡献占主导的奇异值对降噪后信号的信息贡献量。?-SVD降噪算法涉及延迟时间、嵌入维数、降噪阶次、噪声功率和拉格朗日乘子等5个参数。讨论了?-SVD降噪算法的参数选择方法,并着重研究降噪阶次和拉格朗日乘子对降噪效果的影响。齿轮故障仿真信号和齿轮早期裂纹故障振动信号的试验结果表明,?-SVD降噪算法在降噪效果方面要优于传统的SVD降噪算法,可以在强背景噪声情况下更好地提取出齿轮的故障特征。In order to extract machinery fault characteristics that are submerged in strong background noise, a general singular value decomposition （SVD） based subspace noise reduction algorithm is applied to signal processing, i.e.,μ-SVD based denoising method. It can be proved that the traditional SVD based denoising method is a special case of theμ-SVD based one whereμ=0.μ-SVD based denoising methodcontains a filter factor that plays a role in restraining information contributions of the noise-domain singular values to the denoised signal.μ-SVD based denoising method involves five parameters, including delay time, embedding dimension, noise reduction order, noise power and Lagrange multiplier. The selection methods for these parameters are discussed. In particular, the effects of noise reduction order and Lagrange multiplier on denoising performance are also studied. The experimental results of simulation signal with local fault and vibration signal with early crack fault in gear demonstrate that theμ-SVD based denoising method is superior to the traditional one in denoising performance, and can more effectively extract the gear fault characteristics at the presence of strong background noise.

关 键 词：降噪 μ-奇异值分解 滤值因子 故障诊断 齿轮 

分 类 号：TH165[机械工程—机械制造及自动化]