Mining catalytic sensor failure data filtering method based on neural network

Abstract

The invention relates to a mining catalytic sensor failure data filtering method based on a neural network, and belongs to the field of sensor data filtering, and the method comprises the following steps: S1, obtaining a response curve of a mining catalytic sensor; s2, taking the response curve as a sample material for interval sampling, acquiring a plurality of points from the response curve as a group of sample data, and sampling a plurality of groups of sample data; s3, inputting the sample data into a neural network, and constructing and training a failure data recognition model; s4, simulating the failure data identification model; and S5, applying the trained failure data identification model to a sensor system, and filtering failure data. The neural network is applied to the sensor system, failure data are judged and filtered, and the detection precision of the sensor is greatly improved.

Description

technical field [0001] The invention belongs to the field of sensor data filtering, and relates to a neural network-based method for filtering failure data of mining catalytic sensors. Background technique [0002] Coal mine underground sensors such as methane, oxygen, hydrogen sulfide and other sensors constitute the sensing layer of the coal mine safety system and play a vital role in the safety of coal mine production. Most of the current mining sensors are based on the principle of electrochemical catalysis. After the sensor sensitive element contacts the gas to be monitored, it will react electrochemically, which will cause the change of the output voltage of the circuit. According to the change of the output voltage, the concentration of the gas to be monitored can be deduced. When the gas concentration is high, the coal mine safety monitoring system will immediately perform a power-off operation to cut off the underground transmission line to avoid gas explosion disas...

AI Technical Summary

Problems solved by technology

Due to the fact that there will be a certain probability of errors leading to false alarms in the use of electrochemical sensors, and false alarms are often executed by the safety monitoring system as a dangerous signal, which will cause the interruption of the underground power transmission circuit and cause unnecessary losses.

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