A hybrid hierarchical health monitoring solution for autonomous detection, localization and quantification of damage in composite wind turbine blades for tinyML applications.

Composites are widely used in wind turbine blades due to their excellent strength-to-weight ratio and operational flexibilities. However, wind turbines often operate in harsh environmental conditions that can lead to various types of damage, including abrasion, corrosion, fractures, cracks, and delamination. Early detection through structural health monitoring (SHM) is essential for maintaining the efficient and reliable operation of wind turbines, minimizing downtime and maintenance costs, and optimizing energy output. Further, Damage detection and localization are challenging in curved composites due to their anisotropic nature, edge reflections, and generation of higher harmonics. Previous work has focused on damage localization using deep-learning approaches. However, these models are computationally expensive, and multiple models need to be trained independently for various tasks such as damage classification, localization, and sizing identification. Also, the data generated due to AE waveforms at a minimum sampling rate of 1MSPS is huge, requiring tinyML enabled hardware for real time ML models which can reduce the size of cloud storage required. TinyML hardware can run ML models efficiently with low power consumption. This paper presents a Hybrid Hierarchical Machine-Learning Model (HHMLM) that leverages acoustic emission (AE) data to identify, classify, and locate different types of damage using the single unified model. The AE data is collected using a single sensor, with damage simulated by artificial AE sources (Pencil lead break) and low-velocity impacts. Additionally, simulated abrasion on the blade's leading edge resembles environmental wear. This HHMLM model achieved 96.4% overall accuracy with less computation time than 83.8% for separate conventional Convolutional Neural Network (CNN) models. The developed SHM solution provides a more effective and practical solution for in-service monitoring of wind turbine blades, particularly in wind farm settings, with the potential for future wireless sensors with tiny ML applications.