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Detection and identification of windmill bearing faults using a one-class support vector machine (SVM)
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Luleå University of Technology, SKF-LTU University Technology Centre.ORCID iD: 0000-0002-9599-1016
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements. Luleå University of Technology, SKF-LTU University Technology Centre.ORCID iD: 0000-0002-7970-8655
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
SKF (U.K), Livingston, Scotland, United Kingdom.
2019 (English)In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 137, p. 287-301Article in journal (Refereed) Published
Abstract [en]

The maintenance cost of wind turbines needs to be minimized in order to keep their competitiveness and, therefore, effective maintenance strategies are important. The remote location of wind farms has led to an opportunistic maintenance strategy where maintenance actions are postponed until they can be handled simultaneously, once the optimal opportunity has arrived. For this reason, early fault detection and identification are important, but should not lead to a situation where false alarms occur on a regular basis. The goal of the study presented in this paper was to detect and identify wind turbine bearing faults by using fault-specific features extracted from vibration signals. Automatic identification was achieved by training models by using these features as an input for a one-class support vector machine. Detection models with different sensitivity were trained in parallel by changing the model tuning parameters. Efforts were also made to find a procedure for selecting the model tuning parameters by first defining the criticality of the system and using it when estimating how accurate the detection model should be. Method was able to detect the fault earlier than using traditional methods without any false alarms. Optimal combination of features and model tuning parameters was not achieved, which could identify the fault location without using any additional techniques.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 137, p. 287-301
Keywords [en]
Novelty detection, Wind turbine, Bearing fault diagnostics
National Category
Other Civil Engineering Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Operation and Maintenance; Machine Elements
Identifiers
URN: urn:nbn:se:ltu:diva-72839DOI: 10.1016/j.measurement.2019.01.020ISI: 000464553200027Scopus ID: 2-s2.0-85060852953OAI: oai:DiVA.org:ltu-72839DiVA, id: diva2:1287350
Note

Validerad;2019;Nivå 2;2019-02-11 (svasva)

Available from: 2019-02-11 Created: 2019-02-11 Last updated: 2019-04-29Bibliographically approved

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Saari, JuhamattiStrömbergsson, DanielLundberg, Jan

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Operation, Maintenance and AcousticsSKF-LTU University Technology CentreMachine Elements
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Other Civil EngineeringTribology (Interacting Surfaces including Friction, Lubrication and Wear)

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