WT-BTLS-AI Belt Tear Localization AI Detection System

Belt conveyors are among the most widely utilized types of continuous transport equipment, currently finding extensive application in sectors such as ports, mines, steel mills, and power plants. However, factors such as falling liner plates, sharp ferrous objects attracted by iron separators, or improperly installed belt cleaners can cause tearing and abrasion of the conveyor belt, resulting in substantial economic losses. Even if the damage is repairable, the process requires significant manpower and time, thereby having a profound impact on normal operational continuity. Consequently, developing intelligent methods to provide early warnings of belt tearing has emerged as a critical and urgent issue that must be addressed to ensure safe production involving belt conveyor systems.

The AI-powered Integrated Monitoring System employs AI algorithms to analyze images in real-time, distinguishing between normal and anomalous regions. Based on the principles of stereo vision, this detection system offers numerous advantages—including high efficiency, exceptional precision, high speed, and robust environmental adaptability—making it ideally suited for online, non-contact product inspection and quality control within production environments. Particularly in high-speed dynamic inspection scenarios, image acquisition can be completed instantaneously; consequently, the stereo vision approach stands out as a highly effective method for such applications.

The image sensors utilized in this active stereo vision technology feature global shutter capabilities, high frame rates, high resolution, and high dynamic range, serving as specialized hardware components within the camera system. Furthermore, through an intelligent, terminal-based design, the system enables real-time, online, and high-precision inspection of rapidly moving coal flows. It achieves a maximum detection accuracy at the millimeter level, with a detection speed reaching up to 25 frames per second.