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Anomaly detection on factory lathe using audio analysis and deep learning
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This paper presents a master’s thesis project in which a system for anomaly detection on sound of a factory lathe has been developed and evaluated. The audio has been recorded with a microphone on site and has been analyzed using Fourier transforms and a Gated Recurrent Unit, developed to detect when this machine is running. An autoencoder has been used to determine if the gathered audio contains anomalies and thus indicates an error with the machine.

The Gated Recurrent Unit has been evaluated using the metrics Precision, Recall and F1 score along with ROC curves and AUC, which has been used for comparison. To test the autoencoder, artificial anomalies has been generated and used to test if the algorithm gives a higher reconstruction error when these are present in the audio. Both neural networks shows promise, and with further development and training, could possibly work well in a real-life environment.

Place, publisher, year, edition, pages
2019. , p. 56
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:liu:diva-162471ISRN: LIU-ITN-TEK-A-19/053--SEOAI: oai:DiVA.org:liu-162471DiVA, id: diva2:1375776
Subject / course
Media Technology
Uppsok
Technology
Supervisors
Examiners
Available from: 2019-12-06 Created: 2019-12-06 Last updated: 2019-12-06Bibliographically 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