Operational historian for performance based work guidance in maintenance
Aunela, Jaakko Lauri Johannes (2015)
2015
Automaatiotekniikan koulutusohjelma
Teknisten tieteiden tiedekunta - Faculty of Engineering Sciences
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Hyväksymispäivämäärä
2015-04-08
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201503251170
https://urn.fi/URN:NBN:fi:tty-201503251170
Tiivistelmä
As the competition in the pharmaceutical industry grows, Orion, a Finnish pharma company is forced to find new solutions to perform better. One of the solutions is to improve its maintenance to keep its machines running more efficiently. This thesis was done while working at Orion’s Automation department. Orion has a machine monitor-ing system in packaging, but no such system in processes. It is hard to maintain and improve machines without proper monitoring. This thesis examines -what kinds of sys-tems and information should be produced in order to address these problems.
This thesis is divided into two main sections. The theory section covers data storing and performance counters. SCADA or PLC is needed to collect data from machines. OPC DA and A&E technologies are widely used means of collecting data from SCADA to the database, and in the future this will be done by a safer and unified OPC UA. Operational historians are tag-based databases for the process industry that can be used to achieve performance benefits. Big Data consists of items having three common attributes of data collection: volume, velocity and variety. Assets and maintenance management are based on theories on how to understand the assets and how to make the most out of them. Lean Six Sigma is a combination of ideas and tools for making numerous small improvements and stabilizing the process.
Based on these theories and requirements, which were collected from experts repre-senting different departments, a Proof of Concept system was designed and imple-mented. Previously, a floating bed granulator fed data to its SCADA, which had an OPC server running on it. The OPC server fed data to the operational historian data-base and SQL. The new POC system was attached to the existing system to add new functionality, which takes care of OPC DA (process) data and OPC A&E (fault events) data reporting. The POC system now produces reports that contain KPIs on technical usability and top 10 alarm lists.
The system proved to be easily applied to process machines, but needs some mainte-nance for operation. Only minimal knowledge on the machines is needed to create the right reports. The system also seems to perform well and is easily scaled to the com-panywide level. The initial reports focus on maintenance work-related data that can save real time and money. In the near future, the system can provide information to different departments throughout Orion and have some of the functionality of Big Da-ta.
This thesis is divided into two main sections. The theory section covers data storing and performance counters. SCADA or PLC is needed to collect data from machines. OPC DA and A&E technologies are widely used means of collecting data from SCADA to the database, and in the future this will be done by a safer and unified OPC UA. Operational historians are tag-based databases for the process industry that can be used to achieve performance benefits. Big Data consists of items having three common attributes of data collection: volume, velocity and variety. Assets and maintenance management are based on theories on how to understand the assets and how to make the most out of them. Lean Six Sigma is a combination of ideas and tools for making numerous small improvements and stabilizing the process.
Based on these theories and requirements, which were collected from experts repre-senting different departments, a Proof of Concept system was designed and imple-mented. Previously, a floating bed granulator fed data to its SCADA, which had an OPC server running on it. The OPC server fed data to the operational historian data-base and SQL. The new POC system was attached to the existing system to add new functionality, which takes care of OPC DA (process) data and OPC A&E (fault events) data reporting. The POC system now produces reports that contain KPIs on technical usability and top 10 alarm lists.
The system proved to be easily applied to process machines, but needs some mainte-nance for operation. Only minimal knowledge on the machines is needed to create the right reports. The system also seems to perform well and is easily scaled to the com-panywide level. The initial reports focus on maintenance work-related data that can save real time and money. In the near future, the system can provide information to different departments throughout Orion and have some of the functionality of Big Da-ta.