A Novel Framework for Robust Bearing Fault Diagnosis: Preprocessing, Model Selection, and Performance Evaluation-Reference-Cited by-同舟云学术

A Novel Framework for Robust Bearing Fault Diagnosis: Preprocessing, Model Selection, and Performance Evaluation

Published:2024 Issue: Volume:12 Page:59018-59036
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Althobiani Faisal¹^ORCID

Affiliation:

1. Marine Engineering Department, Faculty of Maritime Studies, King Abdulaziz University, Jeddah, Saudi Arabia

Funder

Institutional Fund Projects

Therefore, the authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University, Deanship of Scientific Research (DSR), Jeddah, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10504257.pdf?arnumber=10504257

Reference35 articles.

1. Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

2. Bearing fault detection techniques—A review;Nabhan,2015

3. Review of Condition Monitoring of Rolling Element Bearing Using Vibration Analysis and Other Techniques

4. A labview based condition monitoring program for a wind tunnel based on motor temperature and fan vibration;Reza,2020

5. Rolling bearing fault diagnosis and performance degradation assessment under variable operation conditions based on nuisance attribute projection

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