Accurate Detection of Bearing Faults Using Difference Visibility Graph and Bi-Directional Long Short-Term Memory Network Classifier-Reference-Cited by-同舟云学术

Accurate Detection of Bearing Faults Using Difference Visibility Graph and Bi-Directional Long Short-Term Memory Network Classifier

Published:2022-07 Issue:4 Volume:58 Page:4542-4551
ISSN:0093-9994
Container-title:IEEE Transactions on Industry Applications
language:
Short-container-title:IEEE Trans. on Ind. Applicat.

Author:

Roy Sayanjit Singha¹^ORCID,Chatterjee Soumya²^ORCID,Roy Saptarshi³^ORCID,Bamane Pradip¹,Paramane Ashish¹^ORCID,Rao U. Mohan⁴,Tariq Nazir Muhammad⁵^ORCID

Affiliation:

1. Electrical Engineering Department, NIT Silchar, Assam, India

2. Electrical and Electronics Engineering Department, Birla Institute of Technology, Ranchi, India

3. Electrical and Engineering Department, MirMadan Mohanlal Government Polytechnic, Palasi, India

4. Department of Applied Sciences, University of Quebec at Chicoutimi, Chicoutimi, QC, Canada

5. School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/28/9832711/09760119.pdf?arnumber=9760119

Reference33 articles.

1. Bearing vibration data collected under time-varying rotational speed conditions

2. Semi-Supervised Bearing Fault Diagnosis and Classification Using Variational Autoencoder-Based Deep Generative Models

3. Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine

4. Rolling Element Bearing Fault Diagnosis using Empirical Mode Decomposition and Hjorth Parameters

5. Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings

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