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Data-Driven Fault Diagnosis for Electric Drives: A Review

The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract...

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Autores principales: Gonzalez-Jimenez, David, del-Olmo, Jon, Poza, Javier, Garramiola, Fernando, Madina, Patxi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8230543/
https://www.ncbi.nlm.nih.gov/pubmed/34200893
http://dx.doi.org/10.3390/s21124024
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author Gonzalez-Jimenez, David
del-Olmo, Jon
Poza, Javier
Garramiola, Fernando
Madina, Patxi
author_facet Gonzalez-Jimenez, David
del-Olmo, Jon
Poza, Javier
Garramiola, Fernando
Madina, Patxi
author_sort Gonzalez-Jimenez, David
collection PubMed
description The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities.
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spelling pubmed-82305432021-06-26 Data-Driven Fault Diagnosis for Electric Drives: A Review Gonzalez-Jimenez, David del-Olmo, Jon Poza, Javier Garramiola, Fernando Madina, Patxi Sensors (Basel) Review The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities. MDPI 2021-06-10 /pmc/articles/PMC8230543/ /pubmed/34200893 http://dx.doi.org/10.3390/s21124024 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Gonzalez-Jimenez, David
del-Olmo, Jon
Poza, Javier
Garramiola, Fernando
Madina, Patxi
Data-Driven Fault Diagnosis for Electric Drives: A Review
title Data-Driven Fault Diagnosis for Electric Drives: A Review
title_full Data-Driven Fault Diagnosis for Electric Drives: A Review
title_fullStr Data-Driven Fault Diagnosis for Electric Drives: A Review
title_full_unstemmed Data-Driven Fault Diagnosis for Electric Drives: A Review
title_short Data-Driven Fault Diagnosis for Electric Drives: A Review
title_sort data-driven fault diagnosis for electric drives: a review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8230543/
https://www.ncbi.nlm.nih.gov/pubmed/34200893
http://dx.doi.org/10.3390/s21124024
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