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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8230543 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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