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Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images

As important sources in fault diagnosis of rotary machinery, vibration signals are usually processed in the time or frequency domain as features to distinguish different classes of faults. However, these kinds of processing methods always ignore the corresponding relations among multiple signals, re...

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Autores principales: Yan, Xunshi, Sun, Zhe, Zhao, Jingjing, Shi, Zhengang, Zhang, Chen-An
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359088/
https://www.ncbi.nlm.nih.gov/pubmed/30634612
http://dx.doi.org/10.3390/s19020244
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author Yan, Xunshi
Sun, Zhe
Zhao, Jingjing
Shi, Zhengang
Zhang, Chen-An
author_facet Yan, Xunshi
Sun, Zhe
Zhao, Jingjing
Shi, Zhengang
Zhang, Chen-An
author_sort Yan, Xunshi
collection PubMed
description As important sources in fault diagnosis of rotary machinery, vibration signals are usually processed in the time or frequency domain as features to distinguish different classes of faults. However, these kinds of processing methods always ignore the corresponding relations among multiple signals, resulting in information loss. In this paper, a new fault description strategy named vibration image is proposed, based on which three new kinds of features are extracted, containing coupling information between different channels of vibration signals. Additionally, a new feature fusion method called two-layer AdaBoost is designed to train the fault recognition model, which avoids overfitting when the dataset is not large enough. Features based on vibration images combined with two-layer AdaBoost are adopted to diagnose faults of rotary machinery. Taking an active magnetic bearing-rotor system as the experimental platform, a dataset with four classes of faults is collected and our algorithm achieves good performance. Meanwhile, features based on vibration images and two-layer AdaBoost are both proved to be efficient separately.
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spelling pubmed-63590882019-02-06 Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images Yan, Xunshi Sun, Zhe Zhao, Jingjing Shi, Zhengang Zhang, Chen-An Sensors (Basel) Article As important sources in fault diagnosis of rotary machinery, vibration signals are usually processed in the time or frequency domain as features to distinguish different classes of faults. However, these kinds of processing methods always ignore the corresponding relations among multiple signals, resulting in information loss. In this paper, a new fault description strategy named vibration image is proposed, based on which three new kinds of features are extracted, containing coupling information between different channels of vibration signals. Additionally, a new feature fusion method called two-layer AdaBoost is designed to train the fault recognition model, which avoids overfitting when the dataset is not large enough. Features based on vibration images combined with two-layer AdaBoost are adopted to diagnose faults of rotary machinery. Taking an active magnetic bearing-rotor system as the experimental platform, a dataset with four classes of faults is collected and our algorithm achieves good performance. Meanwhile, features based on vibration images and two-layer AdaBoost are both proved to be efficient separately. MDPI 2019-01-10 /pmc/articles/PMC6359088/ /pubmed/30634612 http://dx.doi.org/10.3390/s19020244 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yan, Xunshi
Sun, Zhe
Zhao, Jingjing
Shi, Zhengang
Zhang, Chen-An
Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images
title Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images
title_full Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images
title_fullStr Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images
title_full_unstemmed Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images
title_short Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images
title_sort fault diagnosis of active magnetic bearing–rotor system via vibration images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359088/
https://www.ncbi.nlm.nih.gov/pubmed/30634612
http://dx.doi.org/10.3390/s19020244
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AT shizhengang faultdiagnosisofactivemagneticbearingrotorsystemviavibrationimages
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