Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis: Preprint

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Improving the availability of wind turbines (WT) is critical to minimize the cost of wind energy, especially for offshore installations. As gearbox downtime has a significant impact on WT availabilities, the development of reliable and cost-effective gearbox condition monitoring systems (CMS) is of great concern to the wind industry. Timely detection and diagnosis of developing gear defects within a gearbox is an essential part of minimizing unplanned downtime of wind turbines. Monitoring signals from WT gearboxes are highly non-stationary as turbine load and speed vary continuously with time. Time-consuming and costly manual handling of large amounts of monitoring data represent ... continued below

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12 p.

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Zappala, D.; Tavner, P.; Crabtree, C. & Sheng, S. January 1, 2013.

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Description

Improving the availability of wind turbines (WT) is critical to minimize the cost of wind energy, especially for offshore installations. As gearbox downtime has a significant impact on WT availabilities, the development of reliable and cost-effective gearbox condition monitoring systems (CMS) is of great concern to the wind industry. Timely detection and diagnosis of developing gear defects within a gearbox is an essential part of minimizing unplanned downtime of wind turbines. Monitoring signals from WT gearboxes are highly non-stationary as turbine load and speed vary continuously with time. Time-consuming and costly manual handling of large amounts of monitoring data represent one of the main limitations of most current CMSs, so automated algorithms are required. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. The algorithm allowed the assessment of gear fault severity by tracking progressive tooth gear damage during variable speed and load operating conditions of the test rig. Results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into WT CMSs, this algorithm can automate data interpretation reducing the quantity of information that WT operators must handle.

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12 p.

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  • To be presented at the European Wind Energy Association 2013 Annual Event, 4-7 February 2013, Vienna, Austria

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  • Report No.: NREL/CP-5000-57395
  • Grant Number: AC36-08GO28308
  • Office of Scientific & Technical Information Report Number: 1063057
  • Archival Resource Key: ark:/67531/metadc835199

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • January 1, 2013

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  • May 19, 2016, 9:45 a.m.

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  • April 3, 2017, 8:06 p.m.

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Zappala, D.; Tavner, P.; Crabtree, C. & Sheng, S. Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis: Preprint, article, January 1, 2013; Golden, Colorado. (digital.library.unt.edu/ark:/67531/metadc835199/: accessed October 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.