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Predictive maintenance with machine learning on weld joint analysed by ultrasound
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Ever since the first industrial revolution industries have had the goal to increase their production. With new technology such as CPS, AI and IoT industries today are going through the fourth industrial revolution denoted as industry 4.0. The new technology not only revolutionises production, but also maintenance, making predictive maintenance possible. Predictive maintenance seeks to predict when failure would occur, instead of having scheduled maintenance or maintenance after failure already occurred. In this report a convolutional neural network (CNN) will analyse data from an ultrasound machine scanning a weld joint. The data from the ultrasound machine will be transformed by the short time Fourier transform in order to create an image for the CNN. Since the data from the ultrasound is not complete, simulated data will be created and investigated as another option for training the network. The results are promising, however the lack of data makes it hard to show any concrete proof.

Place, publisher, year, edition, pages
2019. , p. 26
Series
UPTEC F, ISSN 1401-5757 ; 19058
Keywords [en]
Machine learning, AI, Ultrasound, Predictive maintenance, Industry 4.0
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-396059OAI: oai:DiVA.org:uu-396059DiVA, id: diva2:1366559
External cooperation
Syntronic AB
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2019-11-01 Created: 2019-10-29 Last updated: 2019-11-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
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  • de-DE
  • en-GB
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  • nn-NO
  • nn-NB
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Output format
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