Design of an auxiliary system for quality review of wind turbine operation and maintenance based on deep learning image recognition

Abstract

The cost of operation and maintenance(O&M) has been one of the central budgets in the wind turbines' life cycle. In the final stage of the O&M work, the administrators must manually review the on-site photos to ensure the O&M is qualified, which is time-consuming and ineffective. To improve the efficiency and quality of O&M reviewing work while reducing its costs, we propose an auxiliary reviewing system to optimize the reviewing process. Our system architecture consists of data collection, analysis, and presentation modules. During day times, the data collection module will handle storing and organizing the photos of O&M. The data analysis module will perform duplicate image detection using the MessageDigest Algorithm 5(MD5) and missed image detections through the pre-trained ResNet50 deep learning model. The review results from the analysis module will be fully updated to the database after the analyzing process and rendered into graphs or tables on the webpage when required by the reviewers. Analyses are done in this paper to visualize the functions of each module of the proposed system. The experiment evaluates the performance of three deep learning models, including AlexNet, VGG16, and ResNet50, based on authentic on-site inspection images data from our "first annual inspection" dataset to determine which model yields the best image classification performance. The experimental result reveals that ResNet50 can reach the highest accuracy at 96.2% on the test dataset. Thus, we choose ResNet50 to train the image classifier of our data analysis module.