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Real-time production monitoring system for a small-scale industry based on an open-source platform
University West, Department of Engineering Science.
2022 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In the age of Industry 4.0, where data is the key, data must be collected in real-time and the production processes must be monitored efficiently without the need for human intervention. Having a system that can monitor and gather data automatically from the shop floor and notify managers or supervisors in case of unexpected events helps in enhancing productivity in terms of reduced downtime, increasing process quality, and decision-making process. Monitoring and data collection are made easier by various software available in the market in the form of IoT platforms with a variety of features. However, these software subscriptions prove to be expensive and complicated to implement especially for smallscale industries. The purpose of this thesis work is to transform everyday work processes more conveniently and improve production efficiency by developing a Production monitoring system based on the open-source platform. Therefore, replacing manual decentralized information systems on the shop floor. The research approach adopted consists of information gathered through literature study and the implementation of the solution through an action research approach to obtain the optimal solution. The test conducted on the simulation model of the shop floor and the application developed on the open-source platform showed that the functions of this application are meeting the requirements. Associated people can easily and quickly access information from any computer. As a result, this system will increase the speed of the information flow thus managing the resources more efficiently than before.

Place, publisher, year, edition, pages
2022. , p. 36
Keywords [en]
Industry 4.0, Data collection, Production monitoring system, IIoT
National Category
Robotics and automation
Identifiers
URN: urn:nbn:se:hv:diva-19128Local ID: EXC915OAI: oai:DiVA.org:hv-19128DiVA, id: diva2:1695522
Subject / course
Robotics
Educational program
Master i robotik och automation
Supervisors
Examiners
Available from: 2022-09-23 Created: 2022-09-14 Last updated: 2025-02-09Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf