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

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (21) : 150-156.

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

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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.

Key words

plain bearing / acoustic emission / K-SVD / average signal

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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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