Visual monitoring and structural health assessment method for key components of special equipment

Abstract

To address the issue of structural safety monitoring for key components of special equipment, an engineering technology method based on high-resolution visual feature extraction and structural health assessment is proposed. Through multi-scale convolution networks, precise detection of minor defects is achieved. Combined with state space modeling and time series feature learning, the dynamic prediction of the health status of key components is realized. The introduction of multimodal data fusion and GPU accelerated parallel computing mechanism improves the processing efficiency and stability of the algorithm. By constructing a modular system architecture, the collaborative operation of image acquisition, feature extraction, defect identification, and structural health assessment is realized. Experiments show that this method has obvious advantages in detection accuracy, prediction reliability, and computational efficiency, providing a solid engineering technology foundation for the development of intelligent monitoring technology for key components of special equipment.