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Triaxial bearing vibration dataset of induction motor under varying load conditions

Rotating machines as core component of an industry can effectively be monitored through vibration analysis. Considering the importance of vibration in industrial condition monitoring, this article reports and discusses triaxial vibration data for motor bearing faults detection and identification. Th...

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Detalles Bibliográficos
Autores principales: Kumar, Dileep, Mehran, Sanaullah, Shaikh, Muhammad Zakir, Hussain, Majid, Chowdhry, Bhawani Shankar, Hussain, Tanweer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9156863/
https://www.ncbi.nlm.nih.gov/pubmed/35664656
http://dx.doi.org/10.1016/j.dib.2022.108315
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author Kumar, Dileep
Mehran, Sanaullah
Shaikh, Muhammad Zakir
Hussain, Majid
Chowdhry, Bhawani Shankar
Hussain, Tanweer
author_facet Kumar, Dileep
Mehran, Sanaullah
Shaikh, Muhammad Zakir
Hussain, Majid
Chowdhry, Bhawani Shankar
Hussain, Tanweer
author_sort Kumar, Dileep
collection PubMed
description Rotating machines as core component of an industry can effectively be monitored through vibration analysis. Considering the importance of vibration in industrial condition monitoring, this article reports and discusses triaxial vibration data for motor bearing faults detection and identification. The data is acquired using a MEMS based triaxial accelerometer and the National Instruments myRIO board. The bearing conditions include healthy bearing, bearings with inner race faults, and bearings with outer race faults. For each faulty bearing condition, the three-phase induction motor is operated under three different load conditions. The dataset can be used to assess performance of newly developed methods for effective bearing fault diagnosis. Mendeley Data. http://dx.doi.org/10.17632/fm6xzxnf36.2.
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spelling pubmed-91568632022-06-02 Triaxial bearing vibration dataset of induction motor under varying load conditions Kumar, Dileep Mehran, Sanaullah Shaikh, Muhammad Zakir Hussain, Majid Chowdhry, Bhawani Shankar Hussain, Tanweer Data Brief Data Article Rotating machines as core component of an industry can effectively be monitored through vibration analysis. Considering the importance of vibration in industrial condition monitoring, this article reports and discusses triaxial vibration data for motor bearing faults detection and identification. The data is acquired using a MEMS based triaxial accelerometer and the National Instruments myRIO board. The bearing conditions include healthy bearing, bearings with inner race faults, and bearings with outer race faults. For each faulty bearing condition, the three-phase induction motor is operated under three different load conditions. The dataset can be used to assess performance of newly developed methods for effective bearing fault diagnosis. Mendeley Data. http://dx.doi.org/10.17632/fm6xzxnf36.2. Elsevier 2022-05-23 /pmc/articles/PMC9156863/ /pubmed/35664656 http://dx.doi.org/10.1016/j.dib.2022.108315 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Kumar, Dileep
Mehran, Sanaullah
Shaikh, Muhammad Zakir
Hussain, Majid
Chowdhry, Bhawani Shankar
Hussain, Tanweer
Triaxial bearing vibration dataset of induction motor under varying load conditions
title Triaxial bearing vibration dataset of induction motor under varying load conditions
title_full Triaxial bearing vibration dataset of induction motor under varying load conditions
title_fullStr Triaxial bearing vibration dataset of induction motor under varying load conditions
title_full_unstemmed Triaxial bearing vibration dataset of induction motor under varying load conditions
title_short Triaxial bearing vibration dataset of induction motor under varying load conditions
title_sort triaxial bearing vibration dataset of induction motor under varying load conditions
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9156863/
https://www.ncbi.nlm.nih.gov/pubmed/35664656
http://dx.doi.org/10.1016/j.dib.2022.108315
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