Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.

In the coal production system, the mine main drainage system will consume much electricity. But traditional control strategies cannot simultaneously address water level security and electricity bills. This study proposes the “avoid peak and arrive valley” drainage control strategy based on fuzzy control theory. Water level, water level change rate, and stage electricity price are also considered. Based on ensuring the safe water level, the number of starting pumps is increased to reduce the water level in the low electricity price stage. Next, the Simulink simulation model of the above control strategy is built in MATLAB. The simulation results show that electricity bills decreased by 19.8% relative to the traditional control strategy when the water level is safe. Then, the working state of the mine drainage system is simulated by sinusoidal disturbance, step disturbance, and setting different initial water levels. The stability and robustness of the above intelligent control algorithm are verified. Finally, the offline fuzzy control table is established, and the online control test of the mine drainage pump is realized in the Siemens S7-1200 controller, which verifies the feasibility and stability of the intelligent control strategy. This optimized control method provides a solution for deep mine drainage systems that combines operational safety and economic efficiency.