WP2

About the Workpackage

WP2 focuses on the design of a two-stage AI-based condition monitoring (CM) and structural health monitoring (SHM) platform for the remote assessment of the health of operating blades. The first stage consists of a continuous framework relying on drivetrain CM data for damage identification, whereas the second stage involves a discontinuous AI-based SHM framework for damage location, diagnosis and prognosis, based on damage signatures from WP1. In contrast with models to date focusing on individual signal monitoring techniques and able to target limited types of damage, the proposed platform aims at delivering a comprehensive and cost-effective blade health monitoring system based on complementary advanced sensing methodologies.

Outputs

• Wind turbine high-fidelity dynamic model
• Relationship between CMS signals and blade faults
• Simulated CMS drive train dataset
• AI – based toolbox for damage localization and diagnosis
• Prognostic model for estimation of the remaining useful life of the blade
• Drive-train vibration data can be used to maintenance teams to detect blade faults