An effective approach to rolling bearing diagnosis based on Adaptive Redundant Second-Generation Wavelet

Huaxin Chen; Xuefeng Chen; Yanyang Zi; Feng Ding; Hongrui Cao; Jiyong Tan; Hongkai Jiang; Zhengjia He

doi:10.1504/IJMPT.2008.019773

An effective approach to rolling bearing diagnosis based on Adaptive Redundant Second-Generation Wavelet

Huaxin Chen, Xuefeng Chen, Yanyang Zi, Feng Ding, Hongrui Cao, Jiyong Tan, Hongkai Jiang, Zhengjia He

Xi'an Jiaotong University

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

De-noising and extraction of weak signals are crucial to fault prognostics, and the wavelet transform has been widely used in signal de-noising. In this paper, a new method, which combines the Adaptive Redundant Second-Generation Wavelet (ARSGW) and the Hilbert transform, is proposed. The ARSGW is applied to reveal the transient components of the signal in time domain clearly. Then the Hilbert transform is used to extract fault features of rolling bearing from the wavelet packets. The analysis results of the vibration signals from the experiment and the machine tool spindle show that the proposed method can detect the faults of the rolling bearing effectively.

Original language	English
Pages (from-to)	65-78
Number of pages	14
Journal	International Journal of Materials and Product Technology
Volume	33
Issue number	1-2
DOIs	https://doi.org/10.1504/IJMPT.2008.019773
State	Published - Jul 2008
Externally published	Yes

Keywords

Adaptive redundant second-generation wavelet
ARSGW
Hilbert transform
Lifting scheme
Rolling bearing diagnosis

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10.1504/IJMPT.2008.019773

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title = "An effective approach to rolling bearing diagnosis based on Adaptive Redundant Second-Generation Wavelet",

abstract = "De-noising and extraction of weak signals are crucial to fault prognostics, and the wavelet transform has been widely used in signal de-noising. In this paper, a new method, which combines the Adaptive Redundant Second-Generation Wavelet (ARSGW) and the Hilbert transform, is proposed. The ARSGW is applied to reveal the transient components of the signal in time domain clearly. Then the Hilbert transform is used to extract fault features of rolling bearing from the wavelet packets. The analysis results of the vibration signals from the experiment and the machine tool spindle show that the proposed method can detect the faults of the rolling bearing effectively.",

keywords = "Adaptive redundant second-generation wavelet, ARSGW, Hilbert transform, Lifting scheme, Rolling bearing diagnosis",

author = "Huaxin Chen and Xuefeng Chen and Yanyang Zi and Feng Ding and Hongrui Cao and Jiyong Tan and Hongkai Jiang and Zhengjia He",

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T1 - An effective approach to rolling bearing diagnosis based on Adaptive Redundant Second-Generation Wavelet

AU - Chen, Huaxin

AU - Chen, Xuefeng

AU - Zi, Yanyang

AU - Ding, Feng

AU - Cao, Hongrui

AU - Tan, Jiyong

AU - Jiang, Hongkai

AU - He, Zhengjia

PY - 2008/7

Y1 - 2008/7

N2 - De-noising and extraction of weak signals are crucial to fault prognostics, and the wavelet transform has been widely used in signal de-noising. In this paper, a new method, which combines the Adaptive Redundant Second-Generation Wavelet (ARSGW) and the Hilbert transform, is proposed. The ARSGW is applied to reveal the transient components of the signal in time domain clearly. Then the Hilbert transform is used to extract fault features of rolling bearing from the wavelet packets. The analysis results of the vibration signals from the experiment and the machine tool spindle show that the proposed method can detect the faults of the rolling bearing effectively.

AB - De-noising and extraction of weak signals are crucial to fault prognostics, and the wavelet transform has been widely used in signal de-noising. In this paper, a new method, which combines the Adaptive Redundant Second-Generation Wavelet (ARSGW) and the Hilbert transform, is proposed. The ARSGW is applied to reveal the transient components of the signal in time domain clearly. Then the Hilbert transform is used to extract fault features of rolling bearing from the wavelet packets. The analysis results of the vibration signals from the experiment and the machine tool spindle show that the proposed method can detect the faults of the rolling bearing effectively.

KW - Adaptive redundant second-generation wavelet

KW - ARSGW

KW - Hilbert transform

KW - Lifting scheme

KW - Rolling bearing diagnosis

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JO - International Journal of Materials and Product Technology

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An effective approach to rolling bearing diagnosis based on Adaptive Redundant Second-Generation Wavelet

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Keywords

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