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Predictive maintenance for a wood chipper using supervised machine learning
Linköping University, Department of Computer and Information Science.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

With a predictive model that can predict failures of a manufacturing machine, many benefits can be obtained. Unnecessary downtime and accidents can be avoided. In this study a wood chipper which has 12 replaceable knives was examined. The specific task was to create a predictive model that can predict if a knife change is needed or not. To create a predictive model, supervised machine learning was used. Decision forest was the algorithm used in this study. Data samples were collected from vibration measurements. Each sample was labeled with help of ocular inspections of the knives.

Microsoft Azure learning studio was the workspace used to train all models. The data set acquired consist of 106 samples, were only 9 samples belongs to the minority class. Two strategies of training a model were used, with and without oversampling. The result for the best model without oversampling obtained 87.5% precision and 77.8% recall. The best model with oversampling achieved 79% precision and 86.7% recall. This result indicates that the trained models can be useful. However, the validity of the result has been hurt by a small data set and many uncertainness of acquiring the data set.

Place, publisher, year, edition, pages
2018. , p. 39
Keywords [en]
Predictive maintenance, Supervised machine learning, Decision forest, Wood chipper
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-149304ISRN: LIU-IDA/LITH-EX-A--18/027--SEOAI: oai:DiVA.org:liu-149304DiVA, id: diva2:1228297
External cooperation
Holmen; Sogeti
Subject / course
Information Technology
Available from: 2018-06-29 Created: 2018-06-27 Last updated: 2018-06-29Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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