Sensor Placement Optimisation for FastBlade Reaction Frame Health Monitoring Using Machine Learning Approaches

Abstract

FastBlade, a leading centre for testing composite materials and structures, specialises in conducting experimental fatigue testing on tidal turbine blades and composite structures under various load conditions. These experimental fatigue campaigns can sometimes run for months and hence generate vast datasets from numerous sensors used in testing. 
Traditional grid-based sensor deployment often results in redundant data, complicating data processing and analysis. Hence, optimising sensor placement is crucial for efficient data collection and cost-effectiveness. 

This paper addresses the challenge of optimising sensor placement on the FastBlade reaction frame, essential for full-scale tidal turbine testing, by integrating finite element (FE) simulation data with machine learning (ML) algorithms. Our approach leverages validated finite element (FE) models to identify critical stress regions and simulate mechanical responses, generating high-resolution strain datasets. ML algorithms then determine optimal sensor locations, focusing on areas with high strain gradients. The optimised sensor network is validated using calibrated FE models, which provides continuous deformation data across the frame. 

Results show a significant reduction in the quantity of stain gauges or sensors while maintaining accurate monitoring of critical areas, demonstrating the effectiveness and cost-efficiency of our innovative sensor placement strategy.