A smart shop-floor monitoring system through Internet of Things and wireless sensor networks

• Κύριος συγγραφέας: Μήλας, Νικόλαος
• Άλλοι συγγραφείς: Μούρτζης, Δημήτριος
• Μορφή: Thesis
• Γλώσσα: English
• Έκδοση: 2017
• Θέματα: Industry 4.0; Internet of Things; Machine-tool; Wireless sensor networks; Διαδίκτυο των πραγμάτων; Ασύρματα δίκτυα αισθητήρων; 681.2

With the advent of the fourth industrial revolution manufacturing systems are transformed into digital ecosystems. In this transformation, the Internet of Things (IoT) and other emerging technologies pose a major role. Small and medium enterprises (SMEs) in manufacturing, often employ outdated equipment which does not have connectivity capabilities. Therefore, smart sensor systems are required to connect these resources into the digital world. To address this issue, this master thesis presents a monitoring framework and the development of a data acquisition device for machine-tools. The system is designed for installation into the electrical cabinet of the machine-tools and transmits the data related to their operation to a Cloud server via a wireless sensor topology. The data transmission is performed in two levels i.e. locally in the shop-floor using a star wireless sensor network topology coordinated by a microcomputer gateway, and from the microcomputer to Cloud using Internet protocols. The collected data provide information about the status of the resources, along with a set of performance indicators to support decision making. The developed system follows the IoT paradigm in terms of connecting the physical with the cyber world and offering integration capabilities with existing industrial systems. The gateway level supports connectivity with industrial networks through the OPC-UA standard, while the higher level supports integration with industrial software tools via Web-Services. The operation of the system was evaluated in a 3-axis machine-tool under actual machining operations. Moreover, the capabilities for knowledge reuse were validated through the case-based reasoning methodology in the subject of the energy consumption estimation.