Permanent magnet wall-climbing robots are widely used in the maintenance of tanks in the petrochemical industry and the overhaul of large-pressure pipelines in the hydropower industry. One of the difficulties is to achieve the safe and reliable adsorption of wall-climbing robots. Based on the Halbach array, a double-layer superposition magnetic circuit magnetization method is designed in this paper. Under the same constraints, the adsorption force of the permanent magnetic chuck is increased by at least 8% compared with the traditional magnetic circuit design method. Under the working air gap of 1∼9 mm, the average magnetic energy utilization rate is increased by at least 16.46%. This approach not only improves the magnetic energy utilization of the permanent magnetic chuck but also improves the adsorption safety of the wall-climbing robot.

Pressure pipelines are widely used in hydropower generation, oil and gas transmission, and other fields. After years of operation, a pressure pipeline needs regular maintenance to ensure its safety. At present, manual detection methods are unable to meet this demand. An automatic pressure pipeline detection technology is urgently needed to achieve improved efficiency and accuracy. On the basis of the above requirements, a wall‐climbing robot is designed for automatic pressure pipe inspection and maintenance tasks. Moreover, rapid nondestructive testing of welds on the inner surface of pressure pipelines was performed, and a weld tracking function was developed for wall‐climbing robots. We propose an algorithm framework for weld recognition and centerline extraction by combining computer vision technology with traditional image processing technology using visual images. The experimental verification of the wall‐climbing robot designed in this paper and the algorithm framework for weld recognition and centerline extraction were performed based on actual pressure pipelines. The results show that the algorithm framework developed based on the wall‐climbing robot equipped with an industrial camera for pressure pipeline weld detection can achieve greatly improved efficiency, and the actual weld identification accuracy can exceed 90%, which is very meaningful for practical applications.