Research on Industrial Equipment Image and Sensor Data Association Technology Based on Bayesian Fusion Algorithm

Traditional industrial equipment monitoring methods usually rely on a single data source for fault diagnosis and condition assessment, such as image analysis or temperature monitoring. However, this method often fails to fully capture the complex condition changes during equipment operation, especially in modern industrial environments with complex multivariate interactions. To this end, this paper proposes an image and sensor data association technology of industrial equipment based on Bayesian fusion algorithm from the aspects of data acquisition, data preprocessing, and extracting key features of signals. Through experimental verification, compared with the traditional method of using image or sensor data alone, the proposed method has a significant improvement in the accuracy of equipment fault detection. This paper aims to provide a reliable data fusion solution for the condition monitoring and health management of industrial equipment.