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To improve the reliability of coal mine safety monitoring systems we have analyzed the characteristics of a methane sensor, an important component of the monitoring system of production safety in a coal mine and studied the main type and mode of faults when the sensor was used on-line. We introduced a new method based on artificial neural network to detect faults of methane sensors. In addition, using the output information of a single methane sensor, we established a sensor output model of a dynamic non-linear neural network for on-line fault detection. Finally, the fault of the heating wire of the sensor was simulated, indicating that, when the methane sensor had a fault, the predicted output of the neural network clearly deviated from the actual output, exceeding the pre-set threshold and showing that a fault had occurred in the methane sensor. The result shows that the model has good convergence and stability, and is quite capable of meeting the requirements for on-line fault detection of methane sensors.
To improve the reliability of coal mine safety monitoring systems we have analyzed the characteristics of a methane sensor, an important component of the monitoring system of production safety in a coal mine and studied the main type and mode of faults when the sensor was used on- line. We introduced a new method based on artificial neural network to detect faults of methane sensors. we built a new method based on artificial neural network to detect faults of methane sensors. fault detection. Finally, the fault of the heating wire of the sensor was simulated, indicating that, when the methane sensor had a fault, the predicted output of the neural network clearly deviated from the actual output, exceeding the pre-set threshold and showing that a fault occurred occurred in the methane sensor. The result shows that the model has good convergence and stability, and is quite capable of meeting the requirements for on-line fault detec tion of methane sensors.