Research On Coal Mine Gas Large Number Interference Adaptive Filter Based On Immune Recognition Model

Gas is one of the more serious safety hazards in coal mines.Accurate gas detection is important to ensure the safety and normal operation of coal mines.Because the environment under the mine is very complicated,there are various kinds of interference.The signal during the collection process of the methane sensor is easily polluted by the electromagnetic environment under the mine and generates large numbers of disturbances.The large number of disturbances cause the coal mine gas monitoring system to generate false alarms,which brings many hazards to the coal mine.In order to solve the problem of coal mine gas false alarm caused by large number of disturbances,this paper first analyzes the causes of the formation of large numbers of interference,and then analyzes and compares the characteristics of normal signals and large number of interference signals in coal mines.A large number interference recognition model is designed by using the advantage of the immune algorithm's strong recognition ability.The model mainly learns the characteristics of the normal signal and the large number of interference signals in the coal mine gas,and extracts the immune vaccine under study to improve the correctness of identifying large number of interference signals.Secondly,it is necessary to filter out the identified large-number interference.The adaptive filter research has found good results in noise filtering.Therefore,this paper attempts to propose an adaptive filter based on minimum mean square error(LMS)algorithm.Filter out large disturbances in the coal mine.And the LMS algorithm is improved by introducing the forgetting factor and cosine function.The simulation results show that the improved cosine function LMS algorithm has better performance in terms of convergence rate,stability and filtering interference.Finally,the adaptive immune genetic algorithm is used to optimize the parameters of the cosine function improved LMS algorithm,and the advantage of strong search ability is used to find the optimal step factor to design an adaptive filter to filter out the large number of interference signals.In order to verify whether the large-number interference recognition model is effective,the method is to filter the collected data directly by the LMS adaptive filter,compare the processed waveform with the original gas concentration waveform,and then pass the original data through the interference.After the recognition model is identified,it is filtered by the LMS adaptive filter,and the filtered waveform is compared with the original gasconcentration waveform.Comparing the above two simulation results shows the effectiveness of the immune recognition model.Then,the filtering effect of the LMS adaptive filter on the large number of interference signals is verified.The original data is passed through the interference recognition model and filtered by the Butterworth filter and the continuous mean rejection(CME)algorithm respectively.Compared with the filtering method designed in this paper,the waveform analysis of this method shows that the method has a good effect on the filtering of large interfering signals in gas.