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In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology

This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm and 16.6 ms a...

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Autores principales: Chang, Ching-Yuan, Chang, En-Chieh, Huang, Chi-Wen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960834/
https://www.ncbi.nlm.nih.gov/pubmed/31817141
http://dx.doi.org/10.3390/s19245340
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author Chang, Ching-Yuan
Chang, En-Chieh
Huang, Chi-Wen
author_facet Chang, Ching-Yuan
Chang, En-Chieh
Huang, Chi-Wen
author_sort Chang, Ching-Yuan
collection PubMed
description This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm and 16.6 ms after the image calibration and the benchmark of a laser displacement sensor (LDS). The embedded program of machine vision has used zero-mean normalized correlation (ZNCC) and peak finding (PF) for tracking the registered characteristics on the object surface. The calibrated VMS provides time–displacement curves related to both horizontal and vertical directions, promising remote inspections of selected points without attaching additional markers or sensors. The experimental setup of the VMS is cost-effective and uncomplicated, supporting universal combinations between the imaging system and computational devices. The procedures of the proposed scheme are (1) setting up a digital camera, (2) calibrating the imaging system, (3) retrieving the data of image streaming, (4) executing the ZNCC criteria, and providing the time–displacement results of selected points. The experiment setup of the proposed VMS is straightforward and can cooperate with surveillances in industrial environments. The embedded program upgrades the functionality of the camera system from the events monitoring to remote measurement without the additional cost of attaching sensors on motors or targets. Edge nodes equipped with the image-tracking program serve as the physical layer and upload the extracted features to a cloud server via the wireless sensor network (WSN). The VMS can provide customized services under the architecture of the cyber–physical system (CPS), and this research offers an early warning alarm of the mechanical system before unexpected downtime. Based on the smart sensing technology, the in situ diagnosis of industrial motors given from the VMS enables preventative maintenance and contributes to the precision measurement of intelligent automation.
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spelling pubmed-69608342020-01-24 In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology Chang, Ching-Yuan Chang, En-Chieh Huang, Chi-Wen Sensors (Basel) Article This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm and 16.6 ms after the image calibration and the benchmark of a laser displacement sensor (LDS). The embedded program of machine vision has used zero-mean normalized correlation (ZNCC) and peak finding (PF) for tracking the registered characteristics on the object surface. The calibrated VMS provides time–displacement curves related to both horizontal and vertical directions, promising remote inspections of selected points without attaching additional markers or sensors. The experimental setup of the VMS is cost-effective and uncomplicated, supporting universal combinations between the imaging system and computational devices. The procedures of the proposed scheme are (1) setting up a digital camera, (2) calibrating the imaging system, (3) retrieving the data of image streaming, (4) executing the ZNCC criteria, and providing the time–displacement results of selected points. The experiment setup of the proposed VMS is straightforward and can cooperate with surveillances in industrial environments. The embedded program upgrades the functionality of the camera system from the events monitoring to remote measurement without the additional cost of attaching sensors on motors or targets. Edge nodes equipped with the image-tracking program serve as the physical layer and upload the extracted features to a cloud server via the wireless sensor network (WSN). The VMS can provide customized services under the architecture of the cyber–physical system (CPS), and this research offers an early warning alarm of the mechanical system before unexpected downtime. Based on the smart sensing technology, the in situ diagnosis of industrial motors given from the VMS enables preventative maintenance and contributes to the precision measurement of intelligent automation. MDPI 2019-12-04 /pmc/articles/PMC6960834/ /pubmed/31817141 http://dx.doi.org/10.3390/s19245340 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
Chang, Ching-Yuan
Chang, En-Chieh
Huang, Chi-Wen
In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology
title In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology
title_full In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology
title_fullStr In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology
title_full_unstemmed In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology
title_short In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology
title_sort in situ diagnosis of industrial motors by using vision-based smart sensing technology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960834/
https://www.ncbi.nlm.nih.gov/pubmed/31817141
http://dx.doi.org/10.3390/s19245340
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