Compound gear-bearing fault feature extraction using statistical features based on time-frequency method-Reference-Cited by-同舟云学术

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Compound gear-bearing fault feature extraction using statistical features based on time-frequency method

Published:2018-09 Issue: Volume:125 Page:63-77
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Dhamande Laxmikant S.,Chaudhari Mangesh B.

Funder

SRES’s Sanjivani College of Engineering management

Vishwakarma Institute of Technology, Savitribai Phule Pune University, India

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference35 articles.

1. Bearing fault diagnosis based on wavelet transform and fuzzy inference;Lou;Mech. Syst. Signal Process.,2004

2. Detection of rolling bearing defects using discrete wavelet analysis;Djebala;Meccanica,2008

3. Rolling element bearing fault detection using an improved combination of Hilbert and Wavelet transforms;Wang;J. Mech. Sci. Technol.,2009

4. Thrust bearing groove race defect measurement by wavelet decomposition of pre-processed vibration signal;Singh;Meas. J. Int. Meas. Confed.,2013

5. Defect detection in deep groove ball bearing in presence of external vibration using envelope analysis and Duffing oscillator;Patel;Meas. J. Int. Meas. Confed.,2012

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