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A NARX Model-Based Condition Monitoring Method for Rotor Systems

In this study, we developed a data-driven frequency domain analysis method for rotor systems using the NARX (Nonlinear Auto-Regressive with eXternal input) model established by system vibration signals. We propose a model-based index of fault features calculated in a multi-frequency range to facilit...

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Detalles Bibliográficos
Autores principales: Gao, Yi, Yu, Changshuai, Zhu, Yun-Peng, Luo, Zhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422302/
https://www.ncbi.nlm.nih.gov/pubmed/37571661
http://dx.doi.org/10.3390/s23156878
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author Gao, Yi
Yu, Changshuai
Zhu, Yun-Peng
Luo, Zhong
author_facet Gao, Yi
Yu, Changshuai
Zhu, Yun-Peng
Luo, Zhong
author_sort Gao, Yi
collection PubMed
description In this study, we developed a data-driven frequency domain analysis method for rotor systems using the NARX (Nonlinear Auto-Regressive with eXternal input) model established by system vibration signals. We propose a model-based index of fault features calculated in a multi-frequency range to facilitate condition monitoring of rotor systems. Four steps are included in the proposed method. Firstly, displacement vibration signals are collected at multiple monitored rotating speeds. Secondly, the collected signals are processed as output data and the corresponding input data is generated. Then, NARX models are developed with input and output data to characterize the rotor system. Finally, the NRSF (Nonlinear Response Spectrum Function)-based nonlinear fault index is calculated and compared to the healthy condition. An experimental application to the misaligned rotor system is also demonstrated to verify its effectiveness. Our results indicate that the value of the index directly reflects the severity of the misaligned fault.
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spelling pubmed-104223022023-08-13 A NARX Model-Based Condition Monitoring Method for Rotor Systems Gao, Yi Yu, Changshuai Zhu, Yun-Peng Luo, Zhong Sensors (Basel) Article In this study, we developed a data-driven frequency domain analysis method for rotor systems using the NARX (Nonlinear Auto-Regressive with eXternal input) model established by system vibration signals. We propose a model-based index of fault features calculated in a multi-frequency range to facilitate condition monitoring of rotor systems. Four steps are included in the proposed method. Firstly, displacement vibration signals are collected at multiple monitored rotating speeds. Secondly, the collected signals are processed as output data and the corresponding input data is generated. Then, NARX models are developed with input and output data to characterize the rotor system. Finally, the NRSF (Nonlinear Response Spectrum Function)-based nonlinear fault index is calculated and compared to the healthy condition. An experimental application to the misaligned rotor system is also demonstrated to verify its effectiveness. Our results indicate that the value of the index directly reflects the severity of the misaligned fault. MDPI 2023-08-03 /pmc/articles/PMC10422302/ /pubmed/37571661 http://dx.doi.org/10.3390/s23156878 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
Gao, Yi
Yu, Changshuai
Zhu, Yun-Peng
Luo, Zhong
A NARX Model-Based Condition Monitoring Method for Rotor Systems
title A NARX Model-Based Condition Monitoring Method for Rotor Systems
title_full A NARX Model-Based Condition Monitoring Method for Rotor Systems
title_fullStr A NARX Model-Based Condition Monitoring Method for Rotor Systems
title_full_unstemmed A NARX Model-Based Condition Monitoring Method for Rotor Systems
title_short A NARX Model-Based Condition Monitoring Method for Rotor Systems
title_sort narx model-based condition monitoring method for rotor systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422302/
https://www.ncbi.nlm.nih.gov/pubmed/37571661
http://dx.doi.org/10.3390/s23156878
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