In 2012 verloren we Jean Jacques Peters, voormalig ingenieur van het Waterbouwkundig Laboratorium (1964 tot 1979) en internationaal expert in sedimenttransport, rivierhydraulica en -morfologie. Als eerbetoon aan hem hebben we potamology (http://www.potamology.com/) gecreëerd, een virtueel gedenkarchief dat als doel heeft om zijn manier van denken en morfologische aanpak van rivierproblemen in de wereld in stand te houden en te verspreiden.
Het merendeel van z’n werk hebben we toegankelijk gemaakt via onderstaande zoekinterface.
Condition monitoring of wind turbine drivetrain bearings
Gryllias, K.; Qi, J.; Mauricio, A.; Liu, C. (2019). Condition monitoring of wind turbine drivetrain bearings, in: ASME 2019 2nd International Offshore Wind Technical Conference. pp. 10. https://hdl.handle.net/10.1115/IOWTC2019-7603
In: (2019). ASME 2019 2nd International Offshore Wind Technical Conference. American Society of Mechanical Engineers (ASME): New York. ISBN 978-0-7918-5935-3. 434 pp.
The current pace of renewable energy development around the world is unprecedented, with offshore wind in particular proving to be an extremely valuable and reliable energy source. The global installed capacity of offshore wind turbines by the end of 2022 is expected to reach the 46.4 GW, among which 33.9 GW in Europe. Costs are critical for the future success of the offshore wind sector. The industry is pushing hard to make cost reductions to show that offshore wind is economically comparable to conventional fossil fuels. Efficiencies in Operations and Maintenance (O&M) offer potential to achieve significant cost savings as it accounts for around 20% - 30% of overall offshore wind farm costs. One of the most critical and rather complex assembly of onshore, offshore and floating wind turbines is the gearbox. Gearboxes are designed to last till the end of the lifetime of the asset, according to the IEC 61400-4 standards. On the other hand, a recent study over approximately 350 offshore wind turbines indicate that gearboxes might have to be replaced as early as 6.5 years. Therefore sensing and condition monitoring systems for onshore, offshore and floating wind turbines are needed in order to obtain reliable information on the state and condition of different critical parts, focusing towards the detection and/or prediction of damage before it reaches a critical stage. The development and use of such technologies will allow companies to schedule actions at the right time, and thus will help reducing the costs of operation and maintenance, resulting in an increase of wind energy at a competitive price and thus strengthening productivity of the wind energy sector. At the academic level a plethora of methodologies have been proposed during the last decades for the analysis of vibration signatures focusing towards early and accurate fault detection with limited-false alarms and missed detections. Among others, Envelope Analysis is one of the most important methodologies, where an envelope of the vibration signal is estimated, usually after filtering around a selected frequency band excited by impacts due to the faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclostationary Analysis and corresponding methodologies, i.e. the Cyclic Spectral Correlation and the Cyclic Spectral Coherence, have been proved as powerful tools for condition monitoring. On the other hand the application, test and evaluation of such tools on general industrial cases is still rather limited. Therefore the main aim of this paper is the application and evaluation of advanced diagnostic techniques and diagnostic indicators, including the Enhanced Envelope Spectrum and the Spectral Flatness on real world vibration data collected from vibration sensors on gearboxes in multiple wind turbines over an extended period of time of nearly four years. The diagnostic indicators are compared with classical statistic time and frequency indicators, i.e. Kurtosis, Crest Factor etc. and their effectiveness is evaluated based on the successful detection of two failure events.
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