An improved morphological-wavelet threshold de-noising method based acoustic diagnosis for bearing composite faults

FAN Gaozhan,ZHOU Jun,ZHU Kunli

Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (12) : 221-226.

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PDF(2567 KB)
Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (12) : 221-226.

An improved morphological-wavelet threshold de-noising method based acoustic diagnosis for bearing composite faults

  • FAN Gaozhan,ZHOU Jun,ZHU Kunli 
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Abstract

Sound signals of rotating bearing compound faults acquired in the actual field have characteristics of complex noise sources, the strong background noise and the nonlinearity, causing traditional adaptive multi scale morphology filtering are not completely suitable for blind extraction of composite bearing faults. According to these problems, a method based on Improved Adaptive Multi Scale Compound Morphology Filter (IAMSCMF), Improved Wavelet Threshold De-noising Method (IWTDM) and Sparse Component Analysis (SCA) was presented to identify bearing faults. First, IAMSCMF and IWTDM were used to reduce noise and to improve Signal to Noise Rate (SNR), and then, using SCA to separate signals, at last, FFT calculation was used to deal with the spectrum analysis. The results of simulations and real rolling bearing sound signals analysis show that the method can extract the bearing fault characteristics, verifying the effectiveness of the proposed algorithm.

Key words

Improved morphology filtering / improved wavelet threshold de-noising / sparse component analysis / blind extraction / acoustic diagnosis

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FAN Gaozhan,ZHOU Jun,ZHU Kunli . An improved morphological-wavelet threshold de-noising method based acoustic diagnosis for bearing composite faults[J]. Journal of Vibration and Shock, 2020, 39(12): 221-226

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