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Bearing monitoring in the wind turbine drivetrain: A comparative study of the FFT and wavelet transforms
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
Industrial Digitalisation & Solutions, SKF (Sweden), Luleå, Sweden.
2020 (English)In: Wind Energy, ISSN 1095-4244, E-ISSN 1099-1824, Vol. 23, no 6, p. 1381-1393Article in journal (Refereed) Published
Abstract [en]

Wind turbines are often plagued by premature component failures, with drivetrain bearings being particularly subjected to these failures. To identify failing components, vibration condition monitoring has emerged and grown substantially. The fast Fourier transform (FFT) is the major signal processing method of vibrations. Recently, the wavelet transforms have been used more frequently in bearing vibration research, with one alternative being the discrete wavelet transform (DWT). Here, the low‐frequency component of the signal is repeatedly decomposed into approximative and detailed coefficients using a predefined mother wavelet. An extension to this is the wavelet packet transform (WPT), which decomposes the entire frequency domain and stores the wavelet coefficients in packets. How wavelet transforms and FFT compare regarding fault detection in wind turbine drivetrain bearings has been largely overlooked in literature when applied on field data, with non‐ideal placement of sensors and uncertain parameters influencing the measurements. This study consists of a comprehensive comparison of the FFT, a three‐level DWT, and the WPT when applied on enveloped vibration measurements from two 2.5‐MW wind turbine gearbox bearing failures. The frequency content is compared by calculating a robust condition indicator by summation of the harmonics and shaft speed sidebands of the bearing fault frequencies. Results show a higher performance of the WPT when used as a field vibration measurement analysis tool compared with the FFT as it detects one bearing failure earlier and more clearly, leading to a more stable alarm setting and avoidable, costly false alarms.

Place, publisher, year, edition, pages
John Wiley & Sons, 2020. Vol. 23, no 6, p. 1381-1393
Keywords [en]
bearing failure, condition monitoring, discrete wavelet transform, wavelet packet transform, wind turbine gearbox bearings
National Category
Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Machine Elements
Identifiers
URN: urn:nbn:se:ltu:diva-77861DOI: 10.1002/we.2491ISI: 000513910600001Scopus ID: 2-s2.0-85079731480OAI: oai:DiVA.org:ltu-77861DiVA, id: diva2:1397019
Note

Validerad;2020;Nivå 2;2020-06-03 (alebob)

Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-06-03Bibliographically approved

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Strömbergsson, DanielMarklund, PärBerglund, Kim

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