Due to the high risks of manual labor in the steel industry, there is a growing demand for robot-based solutions to replace traditional manpower. Steel companies aim to reduce on-site personnel, minimize accidents, and enhance productivity. This study develops a robotic system to monitor conveyors in ironmaking and detect potential bearing failures. Rollers on belt conveyors contain bearings that emit abnormal noise when worn or damaged. Traditional manual inspection requires workers to approach each roller and listen directly, posing safety risks and inefficiencies. The proposed system detects faulty bearings more quickly and accurately by localizing abnormal sounds. The system comprises a manipulator with a microphone on its end-effector. The microphone collects sound along the conveyor as the manipulator moves to detect noise sources. Once an abnormal bearing is located, faster and more accurate maintenance becomes possible. This robotbased inspection method improves safety, inspection speed, and productivity.
This study introduces an automated robotic system designed to replace manual maintenance in cold rolling mills, where hazardous confined spaces present significant safety risks to workers. To enhance safety and efficiency, we modified a commercial aerial work platform into a teleoperated mobile robot. The system includes a redesigned end-effector equipped with high-pressure cleaning nozzles and a wide-angle camera for visual inspection. Experimental validation in both laboratory and field settings demonstrated the system's maneuverability and effectiveness. The results indicate that this robotic solution can successfully reduce safety hazards by minimizing manual intervention while ensuring high-quality cleaning and inspection in industrial rolling mills.
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