Vibration signal denoising method for drive roller bearing of mine-used belt conveyor

YANG Xiang; TIAN Muqin; LI Lu; SONG Jiancheng; ZHANG Linfeng; WU Jingkun

doi:10.13272/j.issn.1671-251x.2018080013

Volume 45 Issue 3

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Article Navigation > Journal of Mine Automation > 2019 > 45(3): 66-70

YANG Xiang, TIAN Muqin, LI Lu, et al. Vibration signal denoising method for drive roller bearing of mine-used belt conveyor[J]. Industry and Mine Automation, 2019, 45(3): 66-70. doi: 10.13272/j.issn.1671-251x.2018080013

Citation:

YANG Xiang, TIAN Muqin, LI Lu, et al. Vibration signal denoising method for drive roller bearing of mine-used belt conveyor[J]. Industry and Mine Automation, 2019, 45(3): 66-70. doi: 10.13272/j.issn.1671-251x.2018080013

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Vibration signal denoising method for drive roller bearing of mine-used belt conveyor

doi: 10.13272/j.issn.1671-251x.2018080013

YANG Xiang^{1, 2},
TIAN Muqin^{1, 2},
LI Lu^{1, 2},
SONG Jiancheng^{1, 2},
ZHANG Linfeng^{1, 2},
WU Jingkun^{1, 2}

1.
National & Provincial Joint Engineering Laboratory of Mining Intelligent Electrical Apparatus Technology, Taiyuan University of Technology, Taiyuan 030024, China
2.
Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control, Taiyuan University of Technology, Taiyuan 030024, China)

Publish Date: 2019-03-20

Abstract

Abstract

EMD has modal aliasing and ICA requires the number of collected vibration signal should not be less than the number of source signal. In view of the above problems that existed in existing vibration signal denoising method, a vibration signal denoising method for drive roller bearing of mine-used belt conveyor was proposed which was based on EEMD and FastICA. Firstly, collected vibration signal is decomposed by EEMD algorithm, so as to obtain several IMF components at different scales that containing faults characteristic frequency. Then, the IMF components are reconstructed based on correlation coefficient to obtain characteristic signal and virtual noise signal, which are formed into input matrix as input of FastICA algorithm. Finally, FastICA algorithm is used to separate signal and noise to achieve purpose of signal denoising. The experimental results verify feasibility and effectiveness of the method.