Artificial Immunity-based Detection And Filtering Of "Large Number" Interference In Coal Mine Gas Detection

As there are more electromechanical equipment in underground coal mines,the electromagnetic environment is complex,and the large motor will produce a short but very strong electromagnetic pulse interference at the moment of starting.The strong pulse interference will enter into the gas monitoring system and cause "large number" interference,which will cause the false alarm of the monitoring system,and have a serious impact on the safety production of coal mine.Because the "large number" interference signal and the transmission signal of the gas sensor are extremely low frequency signals,the traditional filtering method will filter out the normal alarm signal as well as the interference signal.To address this problem,this study proposes a filtering model for interference identification based on artificial immune algorithm.The waveform data of the interference signal and the normal gas concentration signal are measured by simulated experiments,from which the feature values of the two different signals are extracted,and the immune cluster learning method is used to mark the features of the two signals on the spatial feature area.The clustering center of the interference signal and the clustering center of the normal signal were found to form the interference detector.A negative selection algorithm is used to detect which signal is acquired in real time.If the signal is judged to be normal after passing the identification model,the signal is output directly,while the signal that is judged to be mixed with interference after passing the identification model is filtered by smoothing method for the interference interval and then output.In order to verify the effectiveness of the proposed model,a group of real-time signals collected in the simulation experiment are used to simulate the interference signal recognition and filtering effect of the model..The simulation results show that the model can effectively distinguish whether the signal is an interfering signal or a normal signal;and it can effectively filter out the identified interfering signal.For comparison,the test signals were filtered with Butterworth low-pass filter and sliding average filter,and the results were compared with the filtering results of the proposed method;the results show that the method has better filtering effect on the "large number" of interference signals in the gas monitoring system,and can solve the problem of coal mine gas The results show that this method has better filtering effect on the "large number" interference signal in the gas monitoring system,and can solve the problem of filtering the "large number" interference signal in the coal mine gas monitoring system due to the motor start.