Automating Fault Detection Using Visual Inspection

Timely and efficient maintenance of wind turbines plays a crucial role in maintaining power grid security. Automated visual inspection and predictive maintenance facilitates the early detection of faults and anomalies, thereby reducing downtime of critical equipment and improving operational efficiency.

However, developing a fault detection algorithm may require analyzing vast amounts of data, which poses significant challenges to traditional image processing and computer vision techniques. Recent developments in deep learning and machine learning can be leveraged to accelerate and automate fault detection in wind turbines, thereby addressing these challenges.

Visual inspection for fault detection is used across a variety of industrial applications. Classical techniques from image processing and computer vision are often used to great effect, but deep learning has rapidly increased in importance in image analysis applications. Deep learning offers sophisticated image processing capabilities for developing smarter ways to identify and classify defects in images and videos.

In this talk, you’ll see an end-to-end workflow for developing an automated fault detection algorithm for identifying defects on wind turbine blades. Learn how front-end deep learning techniques, such as autoencoders for anomaly detection, can be applied to automate visual inspection algorithms, complementing classical techniques from image processing and computer vision.