A new rolling bearing fault diagnosis method based on GFT impulse component extraction
A new rolling bearing fault diagnosis method based on GFT impulse component extraction
复制标题
基于GFT脉冲分量提取的滚动轴承故障诊断新方法
DOI：
10.1016/j.ymssp.2016.03.009
复制
发表时间：
2016-12-15
影响因子：
8.4
通讯作者：
Yang, Hanjian
中科院分区：
文献类型：
--
作者：
Ou, Lu;Yu, Dejie;Yang, Hanjian
周期性脉冲是滚动轴承故障的重要指标。从滚动轴承振动信号中提取脉冲分量对故障诊断具有重要意义。本文从流形的角度将振动信号看作是路径图信号，从频谱域的角度研究了振动信号的图傅里叶变换，并将这两种变换引入到振动信号分析中。为了有效地提取冲击分量，定义了一种新的邻接权矩阵，并对滚动轴承振动信号中的冲击分量和谐波分量进行了GFT分析。此外，由于GFT图谱中的脉冲分量主要集中在高阶区域，提出了一种基于GFT脉冲分量提取的滚动轴承故障诊断新方法。该方法首先对振动信号进行广义傅里叶变换，提取其高阶谱系数重构不同的脉冲分量。接着，计算这些脉冲分量的希尔伯特包络谱，并按顺序排列故障特征频率处的包络谱值。在此基础上，选取故障特征频率处具有最大值的包络谱作为最终结果，从而实现对滚动轴承故障的诊断。最后，提出了一个指标KR，这是峰度和希尔伯特包络谱故障特征提取的冲击分量的比率的乘积，以衡量所提出的方法的性能。仿真和实验验证了该方法的可行性和有效性。(C)2016爱思唯尔有限公司版权所有
Periodic impulses are vital indicators of rolling bearing faults. The extraction of impulse components from rolling bearing vibration signals is of great importance for fault diagnosis. In this paper, vibration signals are taken as the path graph signals in a manifold perspective, and the Graph Fourier Transform (GFT) of vibration signals are investigated from the graph spectrum domain, which are both introduced into the vibration signal analysis. To extract the impulse components efficiently, a new adjacency weight matrix is defined, and then the GFT of the impulse component and harmonic component in the rolling bearing vibration signals are analyzed. Furthermore, as the GFT graph spectrum of the impulse component is mainly concentrated in the high-order region, a new rolling bearing fault diagnosis method based on GFT impulse component extraction is proposed. In the proposed method, the GFT of a vibration signal is firstly performed, and its graph spectrum coefficients in the high-order region are extracted to reconstruct different impulse components. Next, the Hilbert envelope spectra of these impulse components are calculated, and the envelope spectrum values at the fault characteristic frequency are arranged in order. Furthermore, the envelope spectrum with the maximum value at the fault characteristic frequency is selected as the final result, from which the rolling bearing fault can be diagnosed. Finally, an index KR, which is the product of the kurtosis and Hilbert envelope spectrum fault feature ratio of the extracted impulse component, is put forward to measure the performance of the proposed method. Simulations and experiments are utilized to demonstrate the feasibility and effectiveness of the proposed method. (C) 2016 Elsevier Ltd. All rights reserved.