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Towards automatic detection of local bearing defects in rotating machines
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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.
Keyword [en]
bearing, condition monitoring, vibration analysis, signal model, prediction, classification, wavelet, morlet, continuous wavelet transform, wavelet packets, matched filter, envelope method, periodisation, rolling element bearings, vibration analysis, fault-diagnosis, identification, extraction, damage, gear
Identifiers
URN: urn:nbn:se:kth:diva-14437DOI: 10.1016/j.ymssp.2003.12.004ISI: 000225868200005Scopus ID: 2-s2.0-4544293192OAI: oai:DiVA.org:kth-14437DiVA: diva2:332478
Note
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2017-12-12Bibliographically approved

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Strömberg, Jan-Olov
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CiteExportLink to record
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  • apa
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