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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Bibliographic Details
Published inApplied sciences Vol. 11; no. 21; p. 9945
Main Authors Chang, Ray-I, Lee, Chia-Yun, Hung, Yu-Hsin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2021
Subjects
Algorithms
Application programming interface
Automation
Communication
Data acquisition
Data analysis
Data processing
Industrial applications
Internet of Things
knowledge discovery in a database
Learning algorithms
Machine learning
Manufacturing
Manufacturing industry
Predictive maintenance
Process planning
Product quality
Quality management
Scientific visualization
Software
Supervisory control and data acquisition
Textile industry
Value creation
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11219945