Research on the Online Monitoring and Fault Early Warning System of Infrared Thermography for Wind Power Equipment Based on Deep Learning Algorithm - Wang, Libo; Fan, Yadong; Wang, Yanbo; Luo, Guowang; Shen, Minqiang; Zhu, Guangnan (Distributed Generation & Alternative Energy Journal, 2025)
With the continuous expansion of the scale of wind power equipment, its operational safety and reliability face higher challenges. In order to achieve efficient fault early warning and intelligent operation and maintenance, this paper proposes an online monitoring and fault early warning system of infrared thermography for wind power equipment based on deep learning algorithms. Through infrared thermography technology, the distribution data of the temperature field on the surface of the equipment is collected in real time. Combined with the improved Convolutional Neural Network (CNN) model, the features of thermography are automatically extracted and classified to identify typical fault modes such as main bearing wear and blade cracks. The system adopts an online monitoring architecture, integrating data preprocessing, model inference and early warning modules to achieve real-time detection of abnormal temperature rise of the equipment and graded fault early warning. The experimental results show that the detection accuracy of this system for common faults is over 95%, and the average response time is less than 3 seconds, which is significantly better than the traditional threshold alarm method. The research results of this paper can provide an intelligent solution for the condition monitoring of wind power equipment, effectively reduce the risk of unplanned downtime, and improve the operation and maintenance efficiency and economic benefits.