Design and Development of Data Collection Framework for Shop Floor Data
Akram, Farhan (2023)
2023
Automaatiotekniikan DI-ohjelma - Master's Programme in Automation Engineering
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2023-05-12
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202304133687
https://urn.fi/URN:NBN:fi:tuni-202304133687
Tiivistelmä
The rapid pace of technological advancements in the industrial world has transformed the traditional factory floor into a smart, digitalized environment. The advent of advanced and innovative manufacturing techniques, coupled with the Internet of Things (IoT), has made shop floor data an essential source of information for industries to monitor and optimize their operations. The ability to collect, process, and analyse shop floor data has become critical to facilitating the digitalization of factories, which can ultimately improve the competitiveness of industries in the global market.
To address this need, the SHOP4CF project aims to develop a comprehensive platform that can facilitate the digitalization of factories. A key module of this project is the Data Collection Framework (DCF), which collects data from various shop floor devices and sensors, analyses, and process data streams, stores the Event Processing in a database and posts the context data to the FIWARE context broker.
In line with this, the proposed master's thesis seeks to design and develop a data collection framework that can effectively collect and process shop floor data and can facilitate the digitalization of factories, which has become essential for industries to monitor and optimize their processes in the era of Industry 4.0. This framework is scalable, flexible, and modular, accommodating various manufacturing processes’ requirements and integrating with different devices and sensors used on the shop floor. Additionally, the framework is equipped with real-time data analysis capabilities, allowing manufacturing managers and engineers to monitor event processing information and optimize them in real-time as well as handle large volumes of data generated by shop floor devices and store it in a centralized database for further analysis.
The thesis aims to contribute to the development of Industry 4.0 and improve the competitiveness of industries in the global market. To achieve this, it proposes research questions to identify the suitable architecture for collecting real-time shop floor data, how the data collection framework and event processing can be utilized to create real-time alerts for a safe shop floor environment, and which industrial communication protocols have been tested for accomplishing data acquisition for industrial shop floors.
Structured into six chapters, this thesis provides a comprehensive analysis of the proposed data collection framework's design, implementation, and testing. The literature review and theoretical background in Chapter two provide an in-depth analysis of vital concepts, such as automation pyramid, ISA-95, communication protocols, MQTT, OPC UA, FIWARE, and MongoDB. The thesis's innovative framework is presented in Chapter three, and Chapter four discusses its implementation. Chapter five examines the testing process and the results obtained from the proposed data collection framework, while Chapter six concludes the thesis and proposes future work to improve the framework.
To address this need, the SHOP4CF project aims to develop a comprehensive platform that can facilitate the digitalization of factories. A key module of this project is the Data Collection Framework (DCF), which collects data from various shop floor devices and sensors, analyses, and process data streams, stores the Event Processing in a database and posts the context data to the FIWARE context broker.
In line with this, the proposed master's thesis seeks to design and develop a data collection framework that can effectively collect and process shop floor data and can facilitate the digitalization of factories, which has become essential for industries to monitor and optimize their processes in the era of Industry 4.0. This framework is scalable, flexible, and modular, accommodating various manufacturing processes’ requirements and integrating with different devices and sensors used on the shop floor. Additionally, the framework is equipped with real-time data analysis capabilities, allowing manufacturing managers and engineers to monitor event processing information and optimize them in real-time as well as handle large volumes of data generated by shop floor devices and store it in a centralized database for further analysis.
The thesis aims to contribute to the development of Industry 4.0 and improve the competitiveness of industries in the global market. To achieve this, it proposes research questions to identify the suitable architecture for collecting real-time shop floor data, how the data collection framework and event processing can be utilized to create real-time alerts for a safe shop floor environment, and which industrial communication protocols have been tested for accomplishing data acquisition for industrial shop floors.
Structured into six chapters, this thesis provides a comprehensive analysis of the proposed data collection framework's design, implementation, and testing. The literature review and theoretical background in Chapter two provide an in-depth analysis of vital concepts, such as automation pyramid, ISA-95, communication protocols, MQTT, OPC UA, FIWARE, and MongoDB. The thesis's innovative framework is presented in Chapter three, and Chapter four discusses its implementation. Chapter five examines the testing process and the results obtained from the proposed data collection framework, while Chapter six concludes the thesis and proposes future work to improve the framework.