Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

Industry 4.0 has remarkably transformed many industries. Supervisory control and data acquisition (SCADA) architecture is important to enable an intelligent and connected manufacturing factory. SCADA is extensively used in many Internet of Things (IoT) applications, including data analytics and data...

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Autores principales:	Ray-I Chang, Chia-Yun Lee, Yu-Hsin Hung
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Internet of Things knowledge discovery in a database machine learning predictive maintenance Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/6564f6d4d7fe443cae4557f1a2654096
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spelling	oai:doaj.org-article:6564f6d4d7fe443cae4557f1a26540962021-11-11T15:02:18ZCloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process10.3390/app112199452076-3417https://doaj.org/article/6564f6d4d7fe443cae4557f1a26540962021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9945https://doaj.org/toc/2076-3417Industry 4.0 has remarkably transformed many industries. Supervisory control and data acquisition (SCADA) architecture is important to enable an intelligent and connected manufacturing factory. SCADA is extensively used in many Internet of Things (IoT) applications, including data analytics and data visualization. Product quality management is important across most manufacturing industries. In this study, we extensively used SCADA to develop a cloud-based analytics module for production quality predictive maintenance (PdM) in Industry 4.0, thus targeting textile manufacturing processes. The proposed module incorporates a complete knowledge discovery in database process. Machine learning algorithms were employed to analyze preprocessed data and provide predictive suggestions for production quality management. Equipment data were analyzed using the proposed system with an average mean-squared error of ~0.0005. The trained module was implemented as an application programming interface for use in IoT applications and third-party systems. This study provides a basis for improving production quality by predicting optimized equipment settings in manufacturing processes in the textile industry.Ray-I ChangChia-Yun LeeYu-Hsin HungMDPI AGarticleInternet of Thingsknowledge discovery in a databasemachine learningpredictive maintenanceTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9945, p 9945 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Internet of Things knowledge discovery in a database machine learning predictive maintenance Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	Internet of Things knowledge discovery in a database machine learning predictive maintenance Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Ray-I Chang Chia-Yun Lee Yu-Hsin Hung Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
description	Industry 4.0 has remarkably transformed many industries. Supervisory control and data acquisition (SCADA) architecture is important to enable an intelligent and connected manufacturing factory. SCADA is extensively used in many Internet of Things (IoT) applications, including data analytics and data visualization. Product quality management is important across most manufacturing industries. In this study, we extensively used SCADA to develop a cloud-based analytics module for production quality predictive maintenance (PdM) in Industry 4.0, thus targeting textile manufacturing processes. The proposed module incorporates a complete knowledge discovery in database process. Machine learning algorithms were employed to analyze preprocessed data and provide predictive suggestions for production quality management. Equipment data were analyzed using the proposed system with an average mean-squared error of ~0.0005. The trained module was implemented as an application programming interface for use in IoT applications and third-party systems. This study provides a basis for improving production quality by predicting optimized equipment settings in manufacturing processes in the textile industry.
format	article
author	Ray-I Chang Chia-Yun Lee Yu-Hsin Hung
author_facet	Ray-I Chang Chia-Yun Lee Yu-Hsin Hung
author_sort	Ray-I Chang
title	Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
title_short	Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
title_full	Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
title_fullStr	Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
title_full_unstemmed	Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
title_sort	cloud-based analytics module for predictive maintenance of the textile manufacturing process
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6564f6d4d7fe443cae4557f1a2654096
work_keys_str_mv	AT rayichang cloudbasedanalyticsmoduleforpredictivemaintenanceofthetextilemanufacturingprocess AT chiayunlee cloudbasedanalyticsmoduleforpredictivemaintenanceofthetextilemanufacturingprocess AT yuhsinhung cloudbasedanalyticsmoduleforpredictivemaintenanceofthetextilemanufacturingprocess
_version_	1718437416443838464

Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

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