@misc{cd32a45005b5420497e16a2779d7b16b,
title = "Wind Turbine Drivetrain Reliability Research - Gearbox Bearing Axial Cracking Failure Mode Example",
abstract = "The U.S. Department of Energy's National Renewable Energy Laboratory and Argonne National Laboratory have been conducting wind turbine drivetrain (formerly gearbox) reliability research for many years. Although the drivetrain focus has not changed, detailed projects are adjusted every few years based on dynamic needs seen in the field across the wind industry. This webinar will walk through the research methodology by using wind turbine gearbox bearing axial cracking failure mode as an example. The detailed steps include: 1) top failure mode identification based on actual failure data collected from project partners, 2) bench-top testing to identify possible contributing factors and formulate a damage metric, 3) physics domain modeling and validation through testing, 4) reliability assessment and prognosis based on the physics domain model and data domain inputs, and further enhancement through machine learning algorithms, using actual wind plant operational and failure event data. Hopefully, the presented work is of interest to the IISE community, and some members can apply their expertise to wind turbine and plant applications, helping enhance wind power generation technology advancement and its broader deployment.",
keywords = "axial cracking, drivetrain, modeling, reliability, wind turbine",
author = "Shawn Sheng and Jon Keller and Caleb Phillips and Yi Guo and Aaron Greco and Arch Desai and Lindy Williams",
year = "2023",
language = "American English",
series = "Presented at the Institute of Industrial and Systems Engineers Webinar, 13 April 2023",
type = "Other",
}