To achieve the EU-climate goal of 100% CO2-free energy production in 2050, 15% to 50% of Europe’s electricity is to come from wind energy.
🎈 Our partner in this challenge Tarucca is creating a system to monitor wind turbines & predict damage, minimising down time!
Extend the lifetime of wind turbine blades by identifying damage based on ambient vibration data 💪🏽
This challenge can be broken down in the following steps:
Damage is represented as the modal parameters (frequency, shape and damping) shifts on the wind turbine blade. The difficulty for the algorithm is that the environment and operational variables (EOVs) impact these parameters (5-10% variations in frequency), so the solution needs to be:
Who we’re looking for
We expect some experience with programming languages and an interest in machine learning. Anyone with the right motivation and ‘proof’ of understanding of the core concepts found in the application form can sign up!
During this challenge you will use / learn these tech skills:
🤖 Convolutional Neural Networks
📈 XGBoost with SHAP
🪅 outlier analysis
You can join as a contributor (12 hours per week commitment for 2 months), coach (2-4 hours per week, only for experienced ML professionals) and teacher (give one relevant ML / domain masterclass).
During the challenge we will arrange for masterclasses on relevant topics like the use of the cloud computation resources Microsoft, IBM & the HPC Lab have made available for us.
The challenge will run from the 17th of November until the 16th of February, & you will collaborate with a diverse team of over 30 international data specialists and domain experts in subteams, all tackling this problem from different angles.
Some important dates:
🥥 Rather than working together to solve what’s clearly an industry-wide problem, everyone appears to be scrambling to figure it out on their own. Trade secrets don’t often pass between corporations, yet that may be exactly what is needed to rid wind power of some of these efficiency problems.
🍉 It is crucial that a developed SHM is reliable and has a low amount of false alarms. Wrong interpretations of data can lead to unnecessary inspections driving up the cost. Differentiating between novelties caused due to the inﬂuence of Environmental and Operational Variables (EOVs) or damage in the structure can help reduce false alarms.