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Mechanical equipment fault diagnosis based on redundant second generation wavelet packet transform

Authors:

Daren YuAuthors Info & Claims

Digital Signal Processing, Volume 20, Issue 1

Pages 276 - 288

https://doi.org/10.1016/j.dsp.2009.04.005

Published: 01 January 2010 Publication History

Abstract

Wavelet transform has been widely used for the vibration signal based mechanical equipment fault diagnosis. However, the decomposition results of the discrete wavelet transform do not possess time invariant property, which may result in the loss of useful information and decrease the classification accuracy of fault diagnosis. To overcome this deficiency, a novel fault diagnosis method based on the redundant second generation wavelet packet transform is proposed. Firstly, the redundant second generation wavelet packet transform is constructed on the basis of second generation wavelet transform and redundant lifting scheme. Secondly, the vibration signals are decomposed by redundant second generation wavelet packet transform and then the faulty features are extracted from the resultant wavelet packet coefficients. Finally, the extracted fault features are given as input to classifiers for identification. The proposed method is applied for the fault diagnosis of gearbox and gasoline engine valve trains. Test results indicate that a better classification performance can be obtained by using the proposed fault diagnosis method in comparison with using second generation wavelet packet transform based method.

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Cited By

Qian CZhu JShen YJiang QZhang Q(2022)Deep Transfer Learning in Mechanical Intelligent Fault Diagnosis: Application and ChallengeNeural Processing Letters10.1007/s11063-021-10719-z54:3(2509-2531)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11063-021-10719-z
Hong SYin J(2018)Remaining Useful Life Prediction of Bearing Based on Deep Perceptron Neural NetworksProceedings of the 2018 2nd International Conference on Big Data and Internet of Things10.1145/3289430.3289438(175-179)Online publication date: 24-Oct-2018
https://dl.acm.org/doi/10.1145/3289430.3289438
Huang WKong FZhao X(2018)Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theoryJournal of Intelligent Manufacturing10.1007/s10845-015-1174-x29:6(1257-1271)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s10845-015-1174-x
Show More Cited By

Mechanical equipment fault diagnosis based on redundant second generation wavelet packet transform

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Information

Published In

cover image Digital Signal Processing

Digital Signal Processing Volume 20, Issue 1

January, 2010

302 pages

ISSN:1051-2004

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Copyright © Elsevier Inc. © 2009.

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 January 2010

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Cited By

Qian CZhu JShen YJiang QZhang Q(2022)Deep Transfer Learning in Mechanical Intelligent Fault Diagnosis: Application and ChallengeNeural Processing Letters10.1007/s11063-021-10719-z54:3(2509-2531)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11063-021-10719-z
Hong SYin J(2018)Remaining Useful Life Prediction of Bearing Based on Deep Perceptron Neural NetworksProceedings of the 2018 2nd International Conference on Big Data and Internet of Things10.1145/3289430.3289438(175-179)Online publication date: 24-Oct-2018
https://dl.acm.org/doi/10.1145/3289430.3289438
Huang WKong FZhao X(2018)Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theoryJournal of Intelligent Manufacturing10.1007/s10845-015-1174-x29:6(1257-1271)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s10845-015-1174-x
Qu JZhang ZGong T(2016)A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusionNeurocomputing10.1016/j.neucom.2015.07.020171:C(837-853)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.07.020
Yan RGao RChen X(2014)Wavelets for fault diagnosis of rotary machinesSignal Processing10.1016/j.sigpro.2013.04.01596(1-15)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1016/j.sigpro.2013.04.015

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