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Image analysis for smart manufacturing
Halmstad University, School of Information Technology.
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The world of industrial manufacturing has changed a lot during the past decades. It has gone from a labour-intensive process of manual control of machines to a fully connected and automated process. The next big leap in industrial manufacturing is known as industry 4.0 or smart manufacturing. With industry 4.0 comes increased integration between IT systems and the factory floor. This change has proven challenging to implement into existing factories many with the intended lifespan of several decades. One of the single most important parameters to measure is the operating hours of each machine. This information can help companies better utilize their resources and save huge amounts of money.  The goal is to develop a solution which can track the operating hours of the machines using image analysis and the signal lights already mounted on the machines. Using methods commonly used for traffic light recognition in autonomous cars, a system with an accuracy of over 99% during the specified conditions, has been developed. It is believed that if more diverse video data becomes available a system, with high reliability that generalizes well, could be developed using similar methodology. 

Abstract [sv]

Industriell tillverkning har förändrats mycket under de senaste decennierna. Det har gått från en process som krävt mycket manuellt arbete till en process som är nästan helt uppkopplad och automatiserad. Nästa stora steg inom industriell tillverkning går under benämningen industri 4.0 eller smart tillverkning. Med industri 4.0 kommer en ökad integration mellan IT-system och fabriksgolvet. Denna förändring har visat sig vara särskilt svår att implementera i redan existerande fabriker som kan ha en förväntad livstid på flera årtionden. En av de viktigaste parametrarna att mäta inom industriell tillverkning är varje maskins operativa timmar. Denna information kan hjälpa företag att bättre utnyttja tillgängliga resurser och därigenom spara stora summor pengar. Målet är att utveckla en lösning som, med hjälp av bildanalys och de signalljus som maskinerna kommer utrustade med, kan mäta maskinernas operativa timmar. Med hjälp av metoder som vanligen används för trafikljusigenkänning i autonoma fordon har ett system med en träffsäkerhet på över 99% under de förutsättningar som presenteras i rapporten utvecklats. Om mer video med större variation blir tillgänglig är det mycket troligt att det går att utveckla ett system som har hög pålitlighet i de flesta produktionsmiljöer. 

Place, publisher, year, edition, pages
2019. , p. 49
Keywords [en]
Stack lights, image analysis, industri 4.0, smart manufacturing
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hh:diva-39856OAI: oai:DiVA.org:hh-39856DiVA, id: diva2:1327012
External cooperation
HMS Industrial Networks
Educational program
Computer Science and Engineering, 300 credits
Supervisors
Examiners
Available from: 2019-06-20 Created: 2019-06-18 Last updated: 2019-06-20Bibliographically approved

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CiteExportLink to record
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