Infogram performance analysis and its enhancement for bearings diagnostics in presence of non-Gaussian noise-Reference-Cited by-同舟云学术

Infogram performance analysis and its enhancement for bearings diagnostics in presence of non-Gaussian noise

Published:2022-05 Issue: Volume:170 Page:108764
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Hebda-Sobkowicz Justyna^ORCID,Zimroz Radosław^ORCID,Wyłomańska Agnieszka^ORCID,Antoni Jerome

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference59 articles.

1. Rolling element bearing diagnostics—A tutorial;Randall;Mech. Syst. Signal Process.,2011

2. Condition monitoring and fault diagnosis of electrical motors—A review;Nandi;IEEE Trans. Energy Convers.,2005

3. Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography;Peng;Mech. Syst. Signal Process.,2004

4. A review on machinery diagnostics and prognostics implementing condition-based maintenance;Jardine;Mech. Syst. Signal Process.,2006

5. Machine learning methods for wind turbine condition monitoring: A review;Stetco;Renew. Energy,2019

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