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Authors: Feng Miao 1 and Ruzhi Feng 2

Affiliations: 1 School of Physical and Electrical Information, Luoyang Normal University, China ; 2 Henan Mechanical and Electrical Vocational College, China

ISBN: 978-989-758-312-4

Keyword(s): Fault feature extraction, Wavelet De-noising, Blind source separation, Rotor

Abstract: In this paper, a new fault feature extraction method is presented based on wavelet transform and blind source separation. At first, wavelet transform is employed to de-noise measured signals to remove the process noise. Then blind source separation based on second order statistics is used to extract blind source signals of the process. The simulation and experiment testing results show the proposed method that compare with other method based on blind source analysis directly with process information can effectively extract the quantitative feature extraction. Finally,the signals of rotor vibration with noise interference were separated successfully using the proposed method.

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Paper citation in several formats:
Miao, F. and Feng, R. (2018). Fault Feature Extraction Method of Rotor Vibration Signals Based on Blind Source Separation and Wavelet Transform.In 3rd International Conference on Electromechanical Control Technology and Transportation - Volume 1: ICECTT, ISBN 978-989-758-312-4, pages 114-118. DOI: 10.5220/0006966001140118

@conference{icectt18,
author={Feng Miao. and Ruzhi Feng.},
title={Fault Feature Extraction Method of Rotor Vibration Signals Based on Blind Source Separation and Wavelet Transform},
booktitle={3rd International Conference on Electromechanical Control Technology and Transportation - Volume 1: ICECTT,},
year={2018},
pages={114-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006966001140118},
isbn={978-989-758-312-4},
}

TY - CONF

JO - 3rd International Conference on Electromechanical Control Technology and Transportation - Volume 1: ICECTT,
TI - Fault Feature Extraction Method of Rotor Vibration Signals Based on Blind Source Separation and Wavelet Transform
SN - 978-989-758-312-4
AU - Miao, F.
AU - Feng, R.
PY - 2018
SP - 114
EP - 118
DO - 10.5220/0006966001140118

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