改进K-SVD算法在曲轴轴承AE信号的去噪研究

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (21) : 150-156.

论文

张峻宁1，张培林1，华春蓉2，秦萍2

作者信息 +

An Improved Bearing AE Signal Denoising Based on K-SVD Algorithms

ZHANG Jun-ning 1, ZHANG Pei-lin 1, HUA Chun-rong 2, QIN Ping 2

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文章历史 +

摘要

针对柴油机曲轴轴承声发射（Acoustic Emission, AE）信号中裂纹特征信息微弱，易与噪声混淆等问题，在K-SVD字典对信号稀疏的基础上，提出一种均值信号改进的K-SVD字典的滑动轴承AE信号去噪算法。首先重组均值信号和扩展到K-SVD信号矩阵中，在实现K-SVD稀疏AE信号矩阵的同时，稀疏提取均值信号的裂纹信号，然后利用K-SVD处理前、后的均值信号提取其中的本底信号，并采用模糊加权均值滤波器对本底信号进行去噪，去除与裂纹信号混淆的噪声，最后根据信号矩阵、稀疏的裂纹信号和去噪后的本底信号得到低信噪比的AE信号。试验结果表明，所提算法有效去除了AE信号中易与裂纹信号混淆的噪声，使故障特征更加明显，成功刻画了滑动轴承不同摩擦状态间的变化。

Abstract

For extraction of the relatively weak crack information contained in plain bearing Acoustic Emission (AE) signals, basing on signal sensibility in K-SVD algorithms, the improved average signal based on K-SVD dictionary is proposed, which solved the above problem. Firstly, the AE signal matrix sparse and pulse signal extraction characteristics is obtained by using the signal reorganization and expansion strategy. Thus it avoids the mixed noise pollution on the AE signal. Secondly, the fuzzy weighted average filter is applied to process the remaining signal,which eliminates the mixed noise pollution on the low amplitude signals. Finally, the average signal in K-SVD is superimposed by the above two-step. Therefore, compared with the traditional K-SVD algorithm, this algorithm can achieve better denoising performance and more obviously fault features. The experimental results show the change of the friction bearings state, which validates the effectiveness of the algorithms.

导出引用

张峻宁1，张培林1，华春蓉2，秦萍2. 改进K-SVD算法在曲轴轴承AE信号的去噪研究[J]. 振动与冲击, 2017, 36(21): 150-156

ZHANG Jun-ning 1, ZHANG Pei-lin 1, HUA Chun-rong 2, QIN Ping 2. An Improved Bearing AE Signal Denoising Based on K-SVD Algorithms[J]. Journal of Vibration and Shock, 2017, 36(21): 150-156

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