基于流形-奇异值熵的滚动轴承故障特征提取  

Fault Feature Extraction Method for Rolling Bearing Based on Manifold and Singular Values Entropy

作　　者：王奉涛[1] 陈守海[1] 闫达文[2] 朱泓[1] 崔立明[1] 王雷[1] 

机构地区：[1]大连理工大学机械工程学院,大连116023 [2]大连理工大学数学科学学院,大连116023

出　　处：《振动．测试与诊断》2016年第2期288-294,401,共7页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51375067);航空科学基金资助项目(20132163010);中央高校基本科研业务费专项资金资助项目(DUT13JS08)

摘　　要：提出一种基于流形-奇异值熵的滚动轴承时频故障特征提取方法。首先,在HHT(Hilbert-Huang transform,简称HHT)时频分析基础上,应用二维流形方法提取信号流行成分以达到对轴承故障特征进行降维和提取敏感参量的目的;然后,定义了奇异值熵来定量衡量不同故障状态下流行成分的差异;最后,将流形奇异值向量与概率神经网络相结合,有效实现了轴承故障样本分类。与一般的考虑欧式空间全局范围最优值的主分量(principal component analysis,简称PCA)方法及以向量为研究对象的一维流形方法不同,该方法直接以二维信息为研究对象,避免了一维流形算法需将二维信息转化为向量带来的信息损失,与PCA方法相比更能发现隐藏在高维数据流形结构中的局部数据特征。工程信号分析验证了该方法的有效性,为准确提取滚动轴承故障特征提供了一种可靠手段。This paper proposes a fault feature extraction method based on manifold and singular values entropy.First,on the basis of HHT time-frequency analysis,a two-dimensional manifold method was used to extract a signal manifold ingredient to reduce dimensions and extract the sensitive parameters of the bearing fault feature.Second,singular values entropy was defined to quantitatively measure the differences of the manifold ingredient under different fault statuses.This novel method differs from the general PCA method in terms of the global scope optimum value of European space,and from the one dimensional manifold method in terms of a vector as the research object.The method directly uses two-dimensional information as the research object and thus avoids information loss for a one-dimensional manifold algorithm in the necessary process that transforms two-dimensional information into a vector.Moreover,it can easily find more local data characteristics hidden in a high-dimensional data manifold structure compared with the PCA method.Finally,a manifold singular value vector combined with a probabilistic neural network was used to achieve bearing fault samples classification effectively.Engineering signal analysis verified the effectiveness of the proposed method.This paper provides a reliable method to accurately extract the rolling bearing fault feature.

关 键 词：流形 奇异值熵 特征提取 滚动轴承 

分 类 号：TH133.3[机械工程—机械制造及自动化] TH113.1