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Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques
Many parameters can be used to express a machine’s condition and to track its evolution through time, such as modal parameters extracted from vibration signals. Operational Modal Analysis (OMA), commonly used to extract modal parameters from systems under operating conditions, was successfully emplo...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920181/ https://www.ncbi.nlm.nih.gov/pubmed/36772703 http://dx.doi.org/10.3390/s23031665 |
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author | Dreher, Nathali Rolon Storti, Gustavo Chaves Machado, Tiago Henrique |
author_facet | Dreher, Nathali Rolon Storti, Gustavo Chaves Machado, Tiago Henrique |
author_sort | Dreher, Nathali Rolon |
collection | PubMed |
description | Many parameters can be used to express a machine’s condition and to track its evolution through time, such as modal parameters extracted from vibration signals. Operational Modal Analysis (OMA), commonly used to extract modal parameters from systems under operating conditions, was successfully employed in many monitoring systems, but its application in rotating machinery is still in development due to the distinct characteristics of this system. To implement efficient monitoring systems based on OMA, it is essential to automatically extract the modal parameters, which several studies have proposed in the literature. However, these algorithms are usually developed to deal with structures that have different characteristics when compared to rotating machinery, and, therefore, work poorly or do not work with this kind of system. Thus, this paper proposes, and has as its main novelty in, a new automated algorithm to carry out modal parameter identification on rotating machinery through OMA. The proposed technique was applied in two different datasets to enable the evaluation of the robustness to different systems and test conditions. It is revealed that the proposed algorithm is suitable for the accurate extraction of frequencies and damping ratios from the stabilization diagram, for both the rotor and the foundation, and only one user defined parameter is required. |
format | Online Article Text |
id | pubmed-9920181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99201812023-02-12 Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques Dreher, Nathali Rolon Storti, Gustavo Chaves Machado, Tiago Henrique Sensors (Basel) Article Many parameters can be used to express a machine’s condition and to track its evolution through time, such as modal parameters extracted from vibration signals. Operational Modal Analysis (OMA), commonly used to extract modal parameters from systems under operating conditions, was successfully employed in many monitoring systems, but its application in rotating machinery is still in development due to the distinct characteristics of this system. To implement efficient monitoring systems based on OMA, it is essential to automatically extract the modal parameters, which several studies have proposed in the literature. However, these algorithms are usually developed to deal with structures that have different characteristics when compared to rotating machinery, and, therefore, work poorly or do not work with this kind of system. Thus, this paper proposes, and has as its main novelty in, a new automated algorithm to carry out modal parameter identification on rotating machinery through OMA. The proposed technique was applied in two different datasets to enable the evaluation of the robustness to different systems and test conditions. It is revealed that the proposed algorithm is suitable for the accurate extraction of frequencies and damping ratios from the stabilization diagram, for both the rotor and the foundation, and only one user defined parameter is required. MDPI 2023-02-02 /pmc/articles/PMC9920181/ /pubmed/36772703 http://dx.doi.org/10.3390/s23031665 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 Dreher, Nathali Rolon Storti, Gustavo Chaves Machado, Tiago Henrique Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques |
title | Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques |
title_full | Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques |
title_fullStr | Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques |
title_full_unstemmed | Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques |
title_short | Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques |
title_sort | automated operational modal analysis for rotating machinery based on clustering techniques |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920181/ https://www.ncbi.nlm.nih.gov/pubmed/36772703 http://dx.doi.org/10.3390/s23031665 |
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