Four-drum synchronous electric traction detection system based on computer vision

Artificial intelligence algorithms face accuracy challenges in the field of synchronous detection of industrial equipment. In order to improve the detection efficiency of the four-roller synchronous electric traction system, a four-roller synchronous electric traction detection system based on computer vision is proposed. High-precision image acquisition is achieved through industrial cameras, and pre-processing techniques such as median filtering, grayscale stretching and opening operation are used to improve image quality. The roll position and speed characteristics are extracted by combining algorithms such as Canny operator, and a multi-parameter fusion evaluation model is established to realize the detection of the four-roller synchronous electric traction state. Experiments show that the research system can accurately identify the abnormal state of the four-roller components under simulated faults and realize reliable fault detection. At the same time, the sensitivity is reduced to a minimum displacement deviation of 0.05mm and a minimum speed deviation of 0.5%. The error is controlled within a maximum absolute error of 0.008mm for displacement deviation and a maximum absolute error of 0.1% for speed deviation. Experiments show that the system significantly improves the detection accuracy of the four-roller synchronous electric traction and meets the design standards.