Abstract:

Belt conveyors are extensively utilized in mining and power industries. In a typical coal mine conveyor system, coal is transported over distances exceeding 2 km, involving more than 20 000 idlers, which far exceeds a reasonable manual inspection capacity. Given that idlers typically have a lifespan of 1–2 years, there is an urgent need for a rapid, cost-effective, and intelligent safety monitoring system. However, current embedded systems face prohibitive replacement costs, while conventional monitoring technologies suffer from inefficiency at low rotational speeds and lack systematic structural optimization frameworks for diverse idler types and parameters. To address these challenges, this paper introduces an integrated, on-site detachable self-powered idler condition monitoring system (ICMS). This system combines energy harvesting based on the magnetic modulation technology with wireless condition monitoring capabilities. Specifically, it develops a data-driven model integrating convolutional neural networks (CNNs) with genetic algorithms (GAs). The conventional testing results show that the data-driven model not only significantly accelerates the parameter response time, but also achieves a prediction accuracy of 92.95%. The in-situ experiments conducted in coal mines demonstrate the system's reliability and monitoring functionality under both no-load and full-load conditions. This research provides an innovative self-powered condition monitoring solution and develops an efficient data-driven model, offering feasible online monitoring approaches for smart mine construction.

Key words: intelligent safety monitoring, self-powered, magnetic modulation, data-driven model, mining conveyor