A Belt Conveyor Blockage Early Warning Algorithm Based on the Difference Degree of Material Flow at the Inlet and Outlet

Belt conveyors are widely used in mining, ports, and industrial production. To address material blockage risks caused by inlet-outlet flow discrepancies, this study proposes an early warning algorithm based on flow difference analysis. The method employs inlet/outlet flow sensors with adaptive denoising, convolution-based correlation coefficient calculation, and trough extraction for time offset estimation. A sliding time window with exponential weighting, data normalization, and dynamic time warping (DTW) are integrated to analyze flow trends through weighted fusion. Experimental results demonstrate the algorithm achieves is greater than 97% accuracy, issuing 1 -second early warnings (including network transmission) in both simulations and real-world scenarios. This approach ensures stable, practical performance, providing strong support for belt conveyor system intelligent maintenance.