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Exploratory Analysis of Acoustic Emissions in Steel using Dictionary Learning
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
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Number of Authors: 5
2016 (English)In: IEEE Ultrasonics Symposium 2016, Tours France, September 18-21, 2016, Piscataway, NJ: IEEE conference proceedings, 2016Conference paper (Refereed)
Abstract [en]

Analysis of acoustic emissions (AE) from steel deformation is a challenging condition monitoring problem due to the high frequencies and data rates involved, and the difficulty to separate signals from noise. The problem to characterize and identify different AE sources calls for methods that goes beyond conventional time and frequency domain analysis. Feature learning is common in the field of machine learning and is successfully used to approximate and classify other kinds of complex signals. Former studies show that AE classification results depend on the choice of predefined features that are extracted from the raw AE signal, but little is known about feature learning in this context. Here we use dictionary learning and sparse coding to optimize a set of shift-invariant features to the AE signal measured in a steel tensile strength test. The specimen undergoes elastic and plastic deformation and eventually cracks. We investigate the learned features and their repetition rates and use principal component analysis (PCA) to illustrate that the resulting sparse AE code is useful for classification of the three strain stages, without reference to the signal amplitude. Therefore, feature learning is a potentially useful approach to the AE analysis problem, which also opens up for further studies of automated methods for anomaly detection in AE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE conference proceedings, 2016.
Keyword [en]
Dictionary Learning, Acoustic Emission
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Tribology
Research subject
Industrial Electronics; Machine Elements
Identifiers
URN: urn:nbn:se:ltu:diva-59776DOI: 10.1109/ULTSYM.2016.7728825ISBN: 978-1-4673-9897-8ISBN: 978-1-4673-9898-5OAI: oai:DiVA.org:ltu-59776DiVA: diva2:1037506
Conference
IEEE Ultrasonics Symposium 2016, Tours France, September 18-21, 2016
Available from: 2016-10-16 Created: 2016-10-16 Last updated: 2016-11-29Bibliographically approved

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Martin del Campo Barraza, SergioSandin, FredrikSchnabel, StephanMarklund, PärDelsing, Jerker
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