Research On Residual Life Prediction Method Of Shearer Cutting Section Based On Deep Learning

The cutting part is the core component of the shearer cutting the coal seam,and its life is the key to the overall reliability of the shearer,and it is very important for the condition monitoring and remaining life prediction of the cutting part.The operating conditions of the cutting part are complex.In the previous prediction of the remaining life of the cutting part,most scholars established the dynamic simulation model of the cutting part gear and bearing to predict its fatigue life,but this cannot directly reflect the cutting part.Real-time state degradation information.Therefore,this paper analyzes the transmission system and operating conditions of the cutting part,installs sensors,collects operating state data,extracts the characteristics of the state data,fuses the data information,and uses the deep learning method to predict the remaining life of the cutting part from the data-driven direction.The main work of this paper includes:Firstly,the characteristics and operating conditions of the transmission system of the cutting part are explained.According to the characteristics of the transmission system of the cutting part and the actual working conditions,the positions and types of sensor measuring points are deployed,the monitoring signals are collected,and the reasons for the noise in the signals are analyzed.Criterion outlier removal method and research A method based on improved wavelet threshold denoising to achieve signal denoising.Secondly,in order to solve the problems of difficulty in extracting the degradation index and low prediction accuracy when predicting the remaining life of the cutting part,a PCA-GRU-based residual life prediction method for the cutting part is studied.Analyze the vibration signal that can directly reflect the operating state of the cutting part,and extract the time domain and frequency domain features of the vibration signal.The multi-domain original feature set of the vibration signal is fused by PCA to obtain an index that can reflect the decline trend of the cutting part.Through the GRU,the correlation between time series data can be learned,and the tracking of the degradation state of the cutting part and the prediction of the remaining life can be realized.Then,aiming at the problems of high latitude and large quantity of state data and the difficulty of fully considering the relevant information of time series in the process of predicting the remaining service life of shearer cutting part,a kind of remaining life of shearer cutting part based on MSCNN-GRU fusion is studied.method of prediction.Build remaining life prediction labels and optimize model structure.The features of different scales are learned through the convolution kernels of different scales of MSCNN,and the detailed features of the data space are extracted for feature fusion.Combined with GRU to extract time-dependent features,the remaining service life is predicted.Finally,by building a simulation experimental platform for the cutting part of the shearer,the whole life cycle data of the experimental platform is obtained,and the remaining life of the cutting part is predicted.Collect experimental data,filter the data,and extract features.Build the remaining life prediction model of the cutting section.In summary,the deep learning-based residual life prediction method of shearer cutting section proposed in this paper can effectively solve the difficulties in extracting degradation indicators,large amount of data,high dimension,and time-series data correlation faced in life prediction of cutting section.It provides certain theoretical guidance for the predictive maintenance of shearers.