Dahua Technology has introduced a new visual inspection solution for wind turbine blades, leveraging its Xinghan large model and smart IoT sensing capabilities to enable online capture, intelligent recognition, risk alerts, and maintenance recommendations. This approach reduces the need for technicians to physically climb towers for every check, allowing defects to be detected earlier and addressed more promptly.
Previously, inspecting wind turbine blades located hundreds of meters above the ground relied primarily on manual labor and drones. Manual inspection of a single turbine typically required four to eight hours, involving long cycles, high costs, and inherent risks associated with working at heights. Drones, meanwhile, were limited by weather conditions, flight safety regulations, and inspection frequency constraints, making it difficult to capture stable, high-resolution imagery consistently while the turbines remained operational.
Today, a specialized vibration-resistant gimbal installed in the nacelle by Dahua Technology acts as a stationed observer in the sky. It is configured with multiple preset capture positions covering the blade root, mid-blade, blade tip, and various surface areas. As blades pass through these preset positions, the gimbal automatically adjusts its angle and zoom to sequentially capture images, with certain points also utilizing the blade pitch state for optimal shots. Through multi-position and multi-angle collection, each rotation of the blades is transformed into clear, analyzable imagery.
The low-frequency vibrations within the nacelle, high-speed blade rotation, long-distance imaging, and variations in daylight all pose challenges to image quality. The equipment integrates vibration resistance and anti-shake technology, automatic offset correction, wide dynamic range image optimization, and long-distance fill lighting to work together seamlessly. Even under harsh conditions such as low temperatures and strong winds, it consistently captures surface details of the blades, transitioning blade inspection from periodic high-altitude operations to continuous online monitoring.
As high-speed rotating blades sweep past the lens, multiple frames are captured per second. The real value lies in filtering out clear and complete blade images from the continuous stream of footage, and then interpreting the subtle changes that are difficult to detect. This image analysis process is carried out collaboratively by a blade segmentation algorithm and a multimodal video understanding large model. The segmentation algorithm calculates the blade's proportion frame by frame, selecting high-quality images from the 30 frames captured per second, while the multimodal large model further identifies defects such as scratches, cracks, delamination, adhesive failure, ice accumulation, and lightning strike marks. Under a single GPU setup, each image can be analyzed in approximately two seconds.
Given the scarcity and dispersed nature of real-world defect samples on wind turbine blades, prototype network technology extracts key defect features from limited samples. This helps the model distinguish typical differences between various defect types, further enhancing recognition accuracy under small-sample conditions. From rapid filtering of valid frames to in-depth identification of subtle defects, both large and small models play distinct roles and work in tandem.
The system not only flags anomalies from vast volumes of imagery but also automatically marks defects, generates risk warnings, and provides maintenance suggestions. This reduces the burden on operations personnel who would otherwise manually review each image, allowing them to focus more effort on on-site verification and resolution. At a wind farm in Gansu Province, more than 20 large wind turbines operate continuously in the wind. Each time blades pass their preset positions, they leave behind status images for analysis. Over six months of operation, the Dahua wind turbine blade visual inspection system captured approximately 150,000 blade images and detected three defective blades on two turbines: one early-stage crack measuring about seven centimeters in length, and two instances of blade delamination. These subtle anomalies hidden hundreds of meters in the air were thereby precisely identified.
For the detected defects, the system automatically marks the relevant images and outputs risk warnings along with maintenance recommendations, providing a basis for on-site verification and repair planning. This enables operations personnel to arrange timely intervention before defects escalate further. As new energy stations continue to evolve toward reduced staffing and intelligent operations, Dahua Technology is consistently advancing the integration of large models and smart sensing capabilities into business scenarios. This makes equipment status more transparent, risk assessment more timely, and maintenance more efficient, helping wind farms enhance equipment management and safeguard both the stable operation of turbines and the reliable supply of clean energy.