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Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks

A study on the gearbox (speed reducer) defect detection models built from the raw vibration signal measured by a triaxial accelerometer and based on convolutional neural networks (CNNs) is presented. Gear faults such as localized pitting, localized wear on helical pinion tooth flanks, and lubricant...

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Detalles Bibliográficos
Autores principales: Lupea, Iulian, Lupea, Mihaiela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647615/
https://www.ncbi.nlm.nih.gov/pubmed/37960469
http://dx.doi.org/10.3390/s23218769
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author Lupea, Iulian
Lupea, Mihaiela
author_facet Lupea, Iulian
Lupea, Mihaiela
author_sort Lupea, Iulian
collection PubMed
description A study on the gearbox (speed reducer) defect detection models built from the raw vibration signal measured by a triaxial accelerometer and based on convolutional neural networks (CNNs) is presented. Gear faults such as localized pitting, localized wear on helical pinion tooth flanks, and lubricant low level are under observation for three rotating velocities of the actuator and three load levels at the reducer output. A deep learning approach, based on 1D-CNN or 2D-CNN, is employed to extract from the vibration image significant signal features that are used further to identify one of the four states (one normal and three defects) of the system, regardless of the selected load level or the speed. The best-performing 1D-CNN-based detection model, with a testing accuracy of 98.91%, was trained on the signals measured on the Y axis along the reducer input shaft direction. The vibration data acquired from the X and Z axes of the accelerometer proved to be less relevant in discriminating the states of the gearbox, the corresponding 1D-CNN-based models achieving 97.15% and 97% testing accuracy. The 2D-CNN-based model, built using the data from all three accelerometer axes, detects the state of the gearbox with an accuracy of 99.63%.
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spelling pubmed-106476152023-10-27 Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks Lupea, Iulian Lupea, Mihaiela Sensors (Basel) Article A study on the gearbox (speed reducer) defect detection models built from the raw vibration signal measured by a triaxial accelerometer and based on convolutional neural networks (CNNs) is presented. Gear faults such as localized pitting, localized wear on helical pinion tooth flanks, and lubricant low level are under observation for three rotating velocities of the actuator and three load levels at the reducer output. A deep learning approach, based on 1D-CNN or 2D-CNN, is employed to extract from the vibration image significant signal features that are used further to identify one of the four states (one normal and three defects) of the system, regardless of the selected load level or the speed. The best-performing 1D-CNN-based detection model, with a testing accuracy of 98.91%, was trained on the signals measured on the Y axis along the reducer input shaft direction. The vibration data acquired from the X and Z axes of the accelerometer proved to be less relevant in discriminating the states of the gearbox, the corresponding 1D-CNN-based models achieving 97.15% and 97% testing accuracy. The 2D-CNN-based model, built using the data from all three accelerometer axes, detects the state of the gearbox with an accuracy of 99.63%. MDPI 2023-10-27 /pmc/articles/PMC10647615/ /pubmed/37960469 http://dx.doi.org/10.3390/s23218769 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
Lupea, Iulian
Lupea, Mihaiela
Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks
title Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks
title_full Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks
title_fullStr Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks
title_full_unstemmed Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks
title_short Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks
title_sort detecting helical gearbox defects from raw vibration signal using convolutional neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647615/
https://www.ncbi.nlm.nih.gov/pubmed/37960469
http://dx.doi.org/10.3390/s23218769
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