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Faulty bearing detection with wavelet feature extraction
2000 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The purpose of this thesis was to implement and test a wavelet based feature extraction method for classification of bearing signals. A specific method, developed by Saito, was thoroughly investigated and implemented in Matlab. This algorithm has been tested on accelerometer signals originating from faulty and functioning bearings. Signals from both laboratory and industrial environments has been analyzed. It has been shown that this method can be used to find faulty bearings if the algorithm has been trained with signals from similar, both functioning and faulty, bearings. Provided these circumstances, it seems as if this method could be more effective, for some cases, than existing FFT methods.

Place, publisher, year, edition, pages
2000.
Keywords [en]
Technology, wavelets, feature extraction, local discriminant bases, linear, discriminant analysis, bearing
Keywords [sv]
Teknik
Identifiers
URN: urn:nbn:se:ltu:diva-47112ISRN: LTU-EX--00/268--SELocal ID: 4b253a73-6c59-41b1-ab40-566510753f12OAI: oai:DiVA.org:ltu-47112DiVA, id: diva2:1020429
Subject / course
Student thesis, at least 30 credits
Educational program
Engineering Physics, master's level
Examiners
Note
Validerat; 20101217 (root)Available from: 2016-10-04 Created: 2016-10-04Bibliographically approved

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CiteExportLink to record
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  • apa
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