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Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders

Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical sign...

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Detalles Bibliográficos
Autores principales: Ates, Cihan, Höfchen, Tobias, Witt, Mario, Koch, Rainer, Bauer, Hans-Jörg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675279/
https://www.ncbi.nlm.nih.gov/pubmed/38005598
http://dx.doi.org/10.3390/s23229212
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author Ates, Cihan
Höfchen, Tobias
Witt, Mario
Koch, Rainer
Bauer, Hans-Jörg
author_facet Ates, Cihan
Höfchen, Tobias
Witt, Mario
Koch, Rainer
Bauer, Hans-Jörg
author_sort Ates, Cihan
collection PubMed
description Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical significance due to the inherent complexity and vital role of these components in mechanical systems. The primary objective of this study is to develop a data-driven methodology for indirectly determining the wear condition by leveraging experimentally collected vibration data. To accomplish this goal, a novel experimental procedure was devised to expedite wear formation on journal bearings. Seventeen bearings were tested and the collected sensor data were employed to evaluate the predictive capabilities of various sensors and mounting configurations. The effects of different downsampling methods and sampling rates on the sensor data were also explored within the framework of feature engineering. The downsampled sensor data were further processed using convolutional autoencoders (CAEs) to extract a latent state vector, which was found to exhibit a strong correlation with the wear state of the bearing. Remarkably, the CAE, trained on unlabeled measurements, demonstrated an impressive performance in wear estimation, achieving an average Pearson coefficient of 91% in four different experimental configurations. In essence, the proposed methodology facilitated an accurate estimation of the wear of the journal bearings, even when working with a limited amount of labeled data.
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spelling pubmed-106752792023-11-16 Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders Ates, Cihan Höfchen, Tobias Witt, Mario Koch, Rainer Bauer, Hans-Jörg Sensors (Basel) Article Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical significance due to the inherent complexity and vital role of these components in mechanical systems. The primary objective of this study is to develop a data-driven methodology for indirectly determining the wear condition by leveraging experimentally collected vibration data. To accomplish this goal, a novel experimental procedure was devised to expedite wear formation on journal bearings. Seventeen bearings were tested and the collected sensor data were employed to evaluate the predictive capabilities of various sensors and mounting configurations. The effects of different downsampling methods and sampling rates on the sensor data were also explored within the framework of feature engineering. The downsampled sensor data were further processed using convolutional autoencoders (CAEs) to extract a latent state vector, which was found to exhibit a strong correlation with the wear state of the bearing. Remarkably, the CAE, trained on unlabeled measurements, demonstrated an impressive performance in wear estimation, achieving an average Pearson coefficient of 91% in four different experimental configurations. In essence, the proposed methodology facilitated an accurate estimation of the wear of the journal bearings, even when working with a limited amount of labeled data. MDPI 2023-11-16 /pmc/articles/PMC10675279/ /pubmed/38005598 http://dx.doi.org/10.3390/s23229212 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
Ates, Cihan
Höfchen, Tobias
Witt, Mario
Koch, Rainer
Bauer, Hans-Jörg
Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders
title Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders
title_full Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders
title_fullStr Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders
title_full_unstemmed Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders
title_short Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders
title_sort vibration-based wear condition estimation of journal bearings using convolutional autoencoders
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675279/
https://www.ncbi.nlm.nih.gov/pubmed/38005598
http://dx.doi.org/10.3390/s23229212
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