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

التفاصيل البيبلوغرافية
العنوان: Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
المؤلفون: Ray-I Chang, Chia-Yun Lee, Yu-Hsin Hung
المصدر: Applied Sciences, Vol 11, Iss 21, p 9945 (2021)
بيانات النشر: MDPI AG, 2021.
سنة النشر: 2021
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: Internet of Things, knowledge discovery in a database, machine learning, predictive maintenance, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/11/21/9945; https://doaj.org/toc/2076-3417
DOI: 10.3390/app11219945
URL الوصول: https://doaj.org/article/6564f6d4d7fe443cae4557f1a2654096
رقم الأكسشن: edsdoj.6564f6d4d7fe443cae4557f1a2654096
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app11219945