A new bearing fault diagnosis approach combining sensitive statistical features with improved multiscale permutation entropy method-Reference-Cited by-同舟云学术

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A new bearing fault diagnosis approach combining sensitive statistical features with improved multiscale permutation entropy method

Published:2021-04 Issue: Volume:218 Page:106883
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Minhas Amrinder Singh,Singh Sukhjeet

Funder

Department of Science and Technology, Ministry of Science and Technology, India

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference55 articles.

1. A classifier fusion system for bearing fault diagnosis;Batista;Expert Syst. Appl.,2013

2. Prognosis of bearing acoustic emission signals using supervised machine learning;Elforjani;IEEE Trans. Ind. Electron.,2017

3. Modeling and prediction of gearbox faults with data-mining algorithms;Verma;J. Solar Energy Eng.,2013

4. Perceptive analysis of bearing defects (Contribution to vibration monitoring);Laissaoui;Appl. Acoust.,2018

5. Nonstationary time series transformation methods: An experimental review;Salles;Knowl.-Based Syst.,2019

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