Data Collection and Information Flow Management Framework for Industrial Systems
Qureshi, Khurshid Ali (2017)
Qureshi, Khurshid Ali
2017
Automation Engineering
Teknisten tieteiden tiedekunta - Faculty of Engineering Sciences
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Hyväksymispäivämäärä
2017-06-07
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201705241509
https://urn.fi/URN:NBN:fi:tty-201705241509
Tiivistelmä
In today’s global era of competitive environment, the importance of data management is critically evaluated. From business decision making to inventory management, the information generated from data which is gathered from processes and systems is incredibly valued for optimization and analysis. Often the data is transferred and integrated to get the information needed for the big picture of the organization. This interoperability reluctance of the rigid legacy systems deployed in organizations is one the major hindrances of information sharing. This issue has been addressed in the thesis with facts and details and its comparatively reliable solution has been presented.
With the evolution of technology, the supply chain is able to categorize the large amount of data into information required by the user in cost effective and time efficient way. Although ERP systems have been a major breakthrough in streamlining the supply chain management (SCM), cloud computing has revolutionized the SCM. For instance, ERP produces a huge amount of data during the production processes, the real time visibility and access to these remote data sources has been made possible by cloud computing.
The aim of this research is to provide a user-friendly data collection framework through which reliable data can be acquired anytime according to the JSON format provided by the user. To achieve this objective, the research has been conducted in two parts: theoretical research and empirical research. In the first part, the detailed theoretical background of data management, legacy systems, information flow and function blocks have been analyzed. Whereas, the empirical research focuses on the cloud based computing platform, called cloud computing network (C2NET), and implementation of two use cases to resolve the data collection and information flow issue in the industrial legacy systems.
The result of this research work proposes a platform for unifying the flow of information from ERP systems with cloud based systems according to the requirements of the user, which in this case is C2NET platform. This key function is done by executing function block based approach supported by PlantCockpit which uses IEC 61499 standard and the service oriented architecture (SOA) project results. The data or messages received from heterogeneous legacy systems are synchronized in the Legacy System Hub and transferred to the target system with the help of REST, SQL and XML adapters. In this way, the integration of legacy system is carried out along with the harmonization of acquired data.
This unification of data from legacy system through cloud computing network has made the efficient and timely collection of accurate and reliable data easier for the user. It allows the user to extract the information from industrial systems readily available according the needs and thus, is able to mitigate the interoperability issue of legacy systems.
With the evolution of technology, the supply chain is able to categorize the large amount of data into information required by the user in cost effective and time efficient way. Although ERP systems have been a major breakthrough in streamlining the supply chain management (SCM), cloud computing has revolutionized the SCM. For instance, ERP produces a huge amount of data during the production processes, the real time visibility and access to these remote data sources has been made possible by cloud computing.
The aim of this research is to provide a user-friendly data collection framework through which reliable data can be acquired anytime according to the JSON format provided by the user. To achieve this objective, the research has been conducted in two parts: theoretical research and empirical research. In the first part, the detailed theoretical background of data management, legacy systems, information flow and function blocks have been analyzed. Whereas, the empirical research focuses on the cloud based computing platform, called cloud computing network (C2NET), and implementation of two use cases to resolve the data collection and information flow issue in the industrial legacy systems.
The result of this research work proposes a platform for unifying the flow of information from ERP systems with cloud based systems according to the requirements of the user, which in this case is C2NET platform. This key function is done by executing function block based approach supported by PlantCockpit which uses IEC 61499 standard and the service oriented architecture (SOA) project results. The data or messages received from heterogeneous legacy systems are synchronized in the Legacy System Hub and transferred to the target system with the help of REST, SQL and XML adapters. In this way, the integration of legacy system is carried out along with the harmonization of acquired data.
This unification of data from legacy system through cloud computing network has made the efficient and timely collection of accurate and reliable data easier for the user. It allows the user to extract the information from industrial systems readily available according the needs and thus, is able to mitigate the interoperability issue of legacy systems.