Intensive data collection system for smart grid and smart building research

Bazenkov, Nikolay; Boldyshev, Boris A.; Dushin, Sergey V.; Фролов, С. А.; Goubko, Mikhail; Korepanov, Vsevolod O.; Sereda, Leonid

doi:10.1109/summa48161.2019.8947469

Cited by 7 publications

(1 citation statement)

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“…The ThingsBoard platform is used for visualization, enabling the monitoring of sensor data, climate sensor mapping, and displaying energy disaggregation algorithm results. [20] A performance evaluation and resource monitoring system for heterogeneous edge devices was developed using open-source software like Docker, Kubernetes, Prometheus, Grafana, and Node IOP Publishing doi:10.1088/1757-899X/1296/1/012006 4 Exporter. This system creates a containerized environment and edge computing-based cluster, allowing for streamlined management, deployment, and real-time monitoring and analysis of CPU and memory usage.…”

Section: Literature and Prior Workmentioning

confidence: 99%

Industrial IoT system for laser-wire direct energy deposition: data collection and visualization of manufacturing process signals

Martikkala,

Wiikinkoski,

Asadi

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Industry 4.0, also known as the Fourth Industrial Revolution, is a term used to describe the current trend of automation and data exchange in manufacturing and other industries. The Internet of Things (IoT) plays a crucial role in Industry 4.0 by connecting devices, machines, and products to the Internet and enabling real-time data exchange. Moreover, additive manufacturing is a key developing manufacturing technology in Industry 4.0. New technologies such as data analysis with Artificial Intelligence and machine vision are widely used in optimization. However, in a lab environment, these technologies depend on the data collected from the process. For such work, the researchers should be able to focus on their core research rather than on the development of infrastructure to collect and analyse the data. This research presents an open software and hardware IoT solution to monitor a laser wire direct energy deposition system installed in a cartesian type 3-axis machine tool. The IoT solution adopts three open-source tools for core issues, such as 1) interoperability, flexibility, and availability; 2) data storage; and 3) data visualization of sensor data and manufacturing process signals. The system architecture is based on one or more edge devices connected to sensors and forwarding their data toward a local API endpoint. The endpoint is created with Node-RED, an open-source visual flow-based development tool for IoT data. Node-RED forwards the data to an open-source InfluxDB database. Finally, the data is visualized with an open-source Grafana application. The system is prototyped, designed, implemented, and tested in a lab environment to monitor a laser-wire direct energy deposition process. The significance of such a flexible IoT data collection system for research and development projects can be integral. Thus, providing savings in time and money can substantially speed up the development of new technologies where the value arises from the sensor data and its analysis.

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Section: Literature and Prior Workmentioning

confidence: 99%