Barely-Visible Surface Crack Detection for Wind Turbine Sustainability
This work addresses the costly and time-consuming maintenance of wind turbines for sustainable energy production, though it is incremental as it focuses on improving detection with a new dataset rather than a fundamental breakthrough.
The paper tackles the problem of detecting barely-visible hairline cracks on wind turbine blades, which are critical for preventing catastrophic damage, by introducing a novel and diverse dataset and detailing an end-to-end deployed detection pipeline that provides automated maintenance recommendations to extend turbine life and efficiency.
The production of wind energy is a crucial part of sustainable development and reducing the reliance on fossil fuels. Maintaining the integrity of wind turbines to produce this energy is a costly and time-consuming task requiring repeated inspection and maintenance. While autonomous drones have proven to make this process more efficient, the algorithms for detecting anomalies to prevent catastrophic damage to turbine blades have fallen behind due to some dangerous defects, such as hairline cracks, being barely-visible. Existing datasets and literature are lacking and tend towards detecting obvious and visible defects in addition to not being geographically diverse. In this paper we introduce a novel and diverse dataset of barely-visible hairline cracks collected from numerous wind turbine inspections. To prove the efficacy of our dataset, we detail our end-to-end deployed turbine crack detection pipeline from the image acquisition stage to the use of predictions in providing automated maintenance recommendations to extend the life and efficiency of wind turbines.