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Grip, Niklas Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.Johansson, Elin Luleå tekniska universitet.Persson, Lars-Erik Sjöberg, Ronny Nåiden Teknik AB.Strömberg, Jan-Olov Department of Mathematics/NADA, Royal Institute of Technology.Show others and affiliations 2005 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 19, no 3, 509-535 p.Article in journal (Refereed) Published

Abstract [en] : In this paper we derive and compare several different vibration analysis techniques for automatic detection of local defects in bearings. Based on a signal model and a discussion on to what extent a good bearing monitoring method should trust it, we present several analysis tools for bearing condition monitoring and conclude that wavelets are especially well suited for this task. Then we describe a large-scale evaluation of several different automatic bearing monitoring methods using 103 laboratory and industrial environment test signals for which the true condition of the bearing is known from visual inspection. We describe the four best performing methods in detail (two wavelet-based, and two based on envelope and periodisation techniques). In our basic implementation, without using historical data or adapting the methods to (roughly) known machine or signal parameters, the four best methods had 9–13% error rate and are all good candidates for further fine-tuning and optimisation. Especially for the wavelet-based methods, there are several potentially performance improving additions, which we finally summarise into a guiding list of suggestion.

Place, publisher, year, edition, pages: 2005. Vol. 19, no 3, 509-535 p.

Research subject: Mathematics

Identifiers: URN: urn:nbn:se:ltu:diva-12481DOI: 10.1016/j.ymssp.2003.12.004Local ID: ba3b6cb0-6fd7-11db-962b-000ea68e967bOAI: diva2:985432

Note: Validerad; 2005; 20061109 (evan)Available from: 2016-09-29 Created: 2016-09-29Bibliographically approved

Autor: Ericsson, Stefan


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