Sessions
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SpeakersPostersProgramme committeePresenters' dashboardData-driven detection of unlikely failures in wind turbines
When: Thursday, 23 June 2022, 16:00 - 17:15
Where: River 6 / sign up required (40 max.)
Session description
Registrations are closed
• Much has been discussed on how to prevent wind turbine failures. But, can we identify deterioration of components in advance and how we can understand the root cause of those failures that are very unlikely to happen. How can we make those failures predictable? How should data be analysed? Which new technologies can play a key role when dealing with these type of failures? What can be shared with the whole industry as learnings to avoid major failures in the future? Understanding the root cause of the different types of failures can ultimately lead to improvements in the design or maintenance strategy.
• In this session we will discuss the challenges and opportunities of Wind Energy digitalization and how data can be used to drive informed maintenance decisions. Lastly an example of an unlikely failure will be presented including the characteristics of the failure, the consequence and how the failure can be avoided using available data.
Session chair
Miriam Marchante Jiménez
Asset Value Engineering Senior Lead Specialist, Ørsted
Presentations
Creating prediction models for critical blade failures with unbalanced datasets
Rasmus Dovnborg Frederiksen
Data Engineer, Siemens Gamesa Renewable Energy
Data-driven detection of main shaft crack development in wind turbines
Emil Vind
Lead Asset Integrity Specialist, Ørsted