Research on intelligent technology for broken chain monitoring on scraper conveyors

To address the issues of existing AI algorithm-based broken chain monitoring technologies for underground coal mine scraper conveyors, including poor online learning ability, low detection accuracy, instability, and inadequate adaptability and reliability in complex scenarios, an online sequential extreme learning machine (OSELM) network was developed by integrating incremental online training into the extreme learning machine (ELM). This approach enabled both offline and real-time online learning. Based on this, an OSELM-based intelligent broken chain monitoring technology for scraper conveyors was proposed. The OSELM network algorithm, trained on a large dataset of underground scraper conveyor chain monitoring images (offline samples), was embedded into an AI camera. The AI camera was installed at the tail of the scraper conveyor to monitor the operation status of the chain in real-time while performing continuous online learning. The AI cameras output control decisions, with recognition results displayed in real-time on the centralized control system platform for the scraper conveyor. The results of industrial tests in underground mining environments demonstrated that the OSELM network exhibited strong self-learning ability, high generalization ability, and robustness. The mean average precision, accuracy, and precision for chain breakage identification on the scraper conveyor reached 98.6%, 99.3%, and 91.7%, respectively, with a detection speed of 205.6 frames per second. The overall performance outperforms models such as Deep Neural Network Fusion Network, RT-DETR, YOLOv5, YOLOv8, and ELM, achieving precise and real-time detection of the chain status of scraper conveyors.