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Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture

Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today’s Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. Maintenance operat...

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Detalles Bibliográficos
Autores principales: de Castro-Cros, Martí, Velasco, Manel, Angulo, Cecilio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921066/
https://www.ncbi.nlm.nih.gov/pubmed/36772276
http://dx.doi.org/10.3390/s23031236
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author de Castro-Cros, Martí
Velasco, Manel
Angulo, Cecilio
author_facet de Castro-Cros, Martí
Velasco, Manel
Angulo, Cecilio
author_sort de Castro-Cros, Martí
collection PubMed
description Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today’s Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. Maintenance operations are mandatory to tackle in all industrial equipment. It is well known that a huge amount of money is invested in operational and maintenance actions in industrial gas turbines (IGTs). In this paper, two variations of autoencoders were used to analyse the performance of an IGT after major maintenance. The data used to analyse IGT conditions were ambient factors, and measurements were performed using several sensors located along the compressor. The condition assessment of the industrial gas turbine compressor revealed significant changes in its operation point after major maintenance; thus, this indicates the need to update the internal operating models to suit the new operational mode as well as the effectiveness of autoencoder-based models in feature extraction. Even though the processing performance was not compromised, the results showed how this autoencoder approach can help to define an indicator of the compressor behaviour in long-term performance.
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spelling pubmed-99210662023-02-12 Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture de Castro-Cros, Martí Velasco, Manel Angulo, Cecilio Sensors (Basel) Article Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today’s Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. Maintenance operations are mandatory to tackle in all industrial equipment. It is well known that a huge amount of money is invested in operational and maintenance actions in industrial gas turbines (IGTs). In this paper, two variations of autoencoders were used to analyse the performance of an IGT after major maintenance. The data used to analyse IGT conditions were ambient factors, and measurements were performed using several sensors located along the compressor. The condition assessment of the industrial gas turbine compressor revealed significant changes in its operation point after major maintenance; thus, this indicates the need to update the internal operating models to suit the new operational mode as well as the effectiveness of autoencoder-based models in feature extraction. Even though the processing performance was not compromised, the results showed how this autoencoder approach can help to define an indicator of the compressor behaviour in long-term performance. MDPI 2023-01-21 /pmc/articles/PMC9921066/ /pubmed/36772276 http://dx.doi.org/10.3390/s23031236 Text en © 2023 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 Article
de Castro-Cros, Martí
Velasco, Manel
Angulo, Cecilio
Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture
title Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture
title_full Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture
title_fullStr Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture
title_full_unstemmed Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture
title_short Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture
title_sort analysis of gas turbine compressor performance after a major maintenance operation using an autoencoder architecture
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921066/
https://www.ncbi.nlm.nih.gov/pubmed/36772276
http://dx.doi.org/10.3390/s23031236
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