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Abstract
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.
Highlights
Internet of things (IoT) is a system of interconnection and communication between physical objects, sensors, and software; it comprises a perception layer, network layer, and application layer
We developed a hybrid method combining the best practice of the existing manufacturing process and to-be Industry 4.0 to analyze equipment data, predict the quality of production, and maintain settings in themethod textile manufacturing challenges, equipment we developed a hybrid combining the best practice of the process
machine learning (ML) was employed in textile manufacturing
Summary
Internet of things (IoT) is a system of interconnection and communication between physical objects, sensors, and software; it comprises a perception layer, network layer, and application layer. IoT technologies include edge components (e.g., device, sensors, and actuators) and network-to-cloud connectivity (e.g., software and system). IoT (IIoT) integrates data, devices, and industrial systems to facilitate automation, monitoring, and intelligence. Due to the rapid development of IIoT-related technologies, large amounts of device data are uploaded to the cloud [1] IIoT is focused on value creation or cocreation by the improved management of industrial assets, production quality, and quantity. The most well-known examples are Microsoft Azure, Amazon Web Services, and Cloudera; in the factory production line, each industry has its unique manufacturing processes and equipment set. Current manufacturing process planning usually depends on the operating experience of senior employees. Some studies examined predictive maintenance (PdM) for product quality by analyzing the equipment set for textile manufacturing. The equipment is extremely expensive; one advanced textile equipment average cost is
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