Research On Denoising And Prediction Of Adit Deformation Monitoring Data Based On CEEMDAN

In mine safety,the deformation monitoring of underground roadway has always been a hot topic for scholars.The fiber grating sensor can be used for roadway deformation monitoring because of its small size,large deformation and convenient layout.Due to the complex underground environment,the data monitored by fiber grating sensors are often accompanied by noise,resulting in the monitoring data curve is not smooth,which is not conducive to the analysis and decision-making of early warning systems.And the rock movement has randomness and uncertainty,it is difficult to achieve the purpose of accurate prediction of roadway deformation by traditional prediction methods.Therefore,this paper combines the denoising algorithm and the algorithm in the field of artificial intelligence,and introduces the fiber grating monitoring roadway deformation system,which can effectively solve the problem of fiber grating data noise and roadway deformation prediction.The data in this paper are from the fiber grating deformation monitoring system of the main adit in Yanghuopan Coal Mine.Through the observation of the historical data curve,it is found that there are a large number of burrs and rectangular sawtooth redundancy problems in the data curve,so it is judged that the noise data is mixed in the data collected by the fiber grating monitoring system.Aiming at the noise problem in the data,three methods of sliding mean,Kalman filter and Complete EEMD with Adaptive Noise(CEEMDAN)are selected to denoise the data.The denoising effect is evaluated by the error between the denoising results and the original data.In order to obtain the optimal prediction neural network structure,the denoised data are normalized,and the prediction research of three network structures,namely,multilayer feedforward(BP)neural network,long short-term memory(LSTM)neural network and LSTM-BP neural network optimized by genetic algorithm,is introduced.In order to compare the prediction effect before and after data processing,the original monitoring data is used to predict by LSTM-BP neural network structure.In this paper,the performance of neural network prediction is evaluated by four evaluation methods:mean squared error(MSE),mean absolute error(MAE),root mean squared error(RMSE)and determination coefficient(R-Square,R2).The experimental results show that the CEEMDAN method for processing data noise has good data smoothness,the minimum MSE and MAE with the original data,the most effective information extracted,and the best effect for processing data noise.After the model structure is optimized by genetic algorithm(GA),the prediction effect of LSTM-BP neural network is the best.The error between the predicted value and the real value is the smallest,and the goodness of fit is the largest,which can reach more than 0.93,and the average goodness of fit is 0.96.The prediction effect of original monitoring data is not as good as that of denoised data in error and goodness of fit.The research on data denoising and deformation prediction in this paper can provide theoretical support and practical basis for roadway safety early warning.