Measuring Ball Joint Faults in Parabolic-Trough Solar Plants with Data Augmentation and Deep Learning

Automatic inspection of parabolic-trough solar plants is key to preventing failures that can harm the environment and the production of green energy. In this work, we propose a novel methodology to inspect ball joints in parabolic trough collectors, which is a relevant problem that is not adequately covered in the literature. Images collected by an Unmanned Aerial Vehicle are segmented using deep learning to extract ball joint components. In order to generate rich training datasets, we develop a novel data augmentation technique by rotating joints and adding synthetic image background, and demonstrate its impact on the object detection accuracy. Then two types of faults are analyzed: fluid leaks, by means of image color filtering; and geometric shape anomalies, by measuring joint angles of the robotic arms. We propose metrics to quantify these faults and evaluate the damage of the inspected components. Our experimental results with images from operating commercial plants show that we can automatically detect leaks and anomalous angular geometry with a low failure rate compared to human labeling.