Incipient Bearing Fault Feature Extraction Based on Minimum Entropy Deconvolution and K-Singular Value Decomposition
Incipient Bearing Fault Feature Extraction Based on Minimum Entropy Deconvolution and K-Singular Value Decomposition
复制标题
基于最小熵反卷积和K奇异值分解的轴承早期故障特征提取
DOI：
10.1115/1.4037419
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Dong GM
中科院分区：
文献类型：
--
作者：
Dong Guangming;Chen Jin;Zhao Fagang;Dong GM
机械状态监测和故障诊断对于早期检测设备故障或故障至关重要，可确保生产过程中的生产率，质量和安全性。针对滚动轴承早期故障特征提取问题，提出了一种基于特征提取的滚动轴承故障特征提取方法.利用信号稀疏表示技术K-奇异值分解（K-SVD）进行研究。在K-SVD中，它的字典是通过机器学习技术从数据中训练出来的，这使得它比预定义的字典更灵活地适应真实的信号的变化。通过对轴承模拟信号和真实的信号的分析表明，K-SVD比小波字典等预定义字典能更好地提取轴承故障特征。然而，在我们的模拟研究中，K-SVD被发现有很大的代表性误差下，重噪声。为了减少噪声的影响，最小熵反卷积（MED）被用作预滤波器。提出了将MED和K-SVD相结合的方法用于轴承早期故障检测。仿真和实验研究验证了该方法的有效性。实验结果表明，该方法能有效地提取出被试轴承在早期故障阶段的脉冲故障特征。
Machinery condition monitoring and fault diagnosis are essential for early detection of equipment malfunctions or failures, which insure productivity, quality, and safety in the manufacturing process. This paper aims at extracting fault features of rolling element bearings at the incipient fault stage. K-singular value decomposition (K-SVD), one technique for sparse representation of signals, is used for study. In K-SVD, its dictionary is trained from data by machine learning techniques, which allows more flexibility to adapt to variation of real signals than the predefined dictionaries. Analysis on simulated bearing signals and real signals shows that K-SVD can give better bearing fault features than the predefined dictionaries such as wavelet dictionaries. However, during our simulation study, K-SVD was found to have large representation error under heavy noise. To reduce the noise effect, minimum entropy deconvolution (MED) is used as a prefilter. The combination of MED and K-SVD is proposed for incipient bearing fault detection. The method is verified by simulation and experimental study. It is shown that the proposed method can effectively extract the impulsive fault feature of the tested bearing at its incipient fault stage.