Improved method for bearing AE signal denoising based on K-SVD algorithms

被引：0
|
作者：
Zhang J. [1 ]
Zhang P. [1 ]
Hua C. [2 ]
Qin P. [2 ]
机构：
[1] Department 7st Ordnance Engineering College, Shijiazhuang, 050003, Hebei
[2] School of Mechanical Engineering, Southwest Jiaotong University, Chengdu
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2017年 / 36卷 / 21期
关键词：
Acoustic emission; Average signal; K-SVD; Plain bearing;
D O I：
10.13465/j.cnki.jvs.2017.21.023
中图分类号：
学科分类号：
摘要：
For extracting the relatively weak crack information contained in plain bearing Acoustic Emission (AE) signals, in cosideration of the signal sensibility of K-SVD algorithms, an improved average signal method based on the K-SVD dictionary was proposed. The sparse and pulse signal extraction characteristics of the AE signal matrix were obtained by using the signal reorganization and expansion strategy, which avoids the mixed noise pollution on the AE signal. Then, a fuzzy weighted average filter was applied to process the remained signal, which eliminates the mixed noise pollution on the low amplitude signals. The superimposition of the average signal in K-SVD was achieved by the above two steps. Compared with the traditional K-SVD algorithm, the improved algorithm can achieve better denoising performance and more obvious fault features. The experimental results show the change of the bearings friction state, which validates the effectiveness of the algorithms at the same time. © 2017, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：150 / 156
页数：6
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