基于改进形态-小波阈值降噪的轴承复合故障声学诊断

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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

Received: 18 December 2018 Published: 15 June 2020

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	Articles by authors
	FAN Gaozhan
	ZHOU Jun
	ZHU Kunli

Cite this article:

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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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2020/V39/I12/221

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