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Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.ORCID iD: 0000-0002-7970-8655
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.ORCID iD: 0000-0003-3157-4632
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.ORCID iD: 0000-0002-8533-897x
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-9599-1016
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2019 (English)In: Wind Energy, ISSN 1095-4244, E-ISSN 1099-1824, Vol. 22, no 11, p. 1581-1592Article in journal (Refereed) Published
Abstract [en]

Although the discrete wavelet transform has been used for diagnosing bearing faults for two decades, most work in this field has been done with test rig data. Since field data starts to be made more available, there is a need to shift into application studies.

The choice of mother wavelet, ie, the predefined shape used to analyse the signal, has previously been investigated with simulated and test rig data without consensus of optimal choice in literature. Common between these investigations is the use of the wavelet coefficients' Shannon entropy to find which mother wavelet can yield the most useful features for condition monitoring.

This study attempts to find the optimal mother wavelet selection using the discrete wavelet transform. Datasets from wind turbine gearbox accelerometers, consisting of enveloped vibration measurements monitoring both healthy and faulty bearings, have been analysed. The bearing fault frequencies' excitation level has been analysed with 130 different mother wavelets, yielding a definitive measure on their performance. Also, the applicability of Shannon entropy as a ranking method of mother wavelets has been investigated.

The results show the discrete wavelet transforms ability to identify faults regardless of mother wavelet used, with the excitation level varying no more than 4%. By analysing the Shannon entropy, broad predictions to the excitation level could be drawn within the mother wavelet families but no direct correlation to the main results. Also, the high computational effort of high order Symlet wavelets, without increased performance, makes them unsuitable.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019. Vol. 22, no 11, p. 1581-1592
Keywords [en]
bearing failure, condition monitoring, discrete wavelet transform, mother wavelet selection, wind turbine field measurements
National Category
Other Civil Engineering Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Machine Elements; Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-75777DOI: 10.1002/we.2390ISI: 000480192400001OAI: oai:DiVA.org:ltu-75777DiVA, id: diva2:1347092
Note

Validerad;2019;Nivå 2;2019-12-06 (johcin)

Available from: 2019-08-30 Created: 2019-08-30 Last updated: 2019-12-06Bibliographically approved

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Strömbergsson, DanielMarklund, PärBerglund, KimSaari, Juhamatti

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Strömbergsson, DanielMarklund, PärBerglund, KimSaari, Juhamatti
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Machine ElementsOperation, Maintenance and Acoustics
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Wind Energy
Other Civil EngineeringTribology (Interacting Surfaces including Friction, Lubrication and Wear)

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