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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...
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/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. |
format | Online Article Text |
id | pubmed-10675279 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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