Remaining Useful Life Prediction For Shearer Key Parts Based On Deep Learning

As one of the main equipment of coal mining,shearer owns the large size and complex structure,and the remaining useful life(RUL)prediction value of its key parts is difficult to obtain accurately due to the influence of poor working environment,narrow operating space and other factors,which leads to the difficulty to predict shearer health status and seriously threatens the safety of coal mine production and the life safety of workers.At present,the method of reliability analysis for the key parts of shearer is mainly limited to the theoretical analysis based on static simulation such as software and mathematical model,and the monitoring data is not used for mining and analysis,resulting in the defects such as analysis results unilateral,inaccurate,low in the analysis efficiency,and lagging behind in the intellectualization.Researching and using advanced theories and methods to mine information from the big data of coal machinery equipment,and efficiently and accurately identify the health status of equipment has become a new problem in the field of coal machinery equipment health monitoring.Combining the advantages of deep learning with extremely strong nonlinear fitting capabilities,a method for the RUL prediction of shearer key parts based on deep learning is proposed.According to the actual degradation trend of monitoring objects and the characteristics of monitoring data,the monitoring data are divided into two types: life cycle and non-life cycle data.The RUL prediction models based on deep neural network are constructed by using the concept of classification and regression respectively,which characterizes the potential non-linear mapping relationship between monitoring data and RUL.The performance of the model is verified by experiments.Based on the model research,the RUL prediction system architecture of the shearer key parts is built.Taking the vulnerable parts of the shearer rocker arm as an example,the feasibility of the two models in RUL prediction of the key parts is analyzed.The main research contents are as follows:(1)The working performance of each part of the shearer is studied.The failure phenomena and reasons of its key parts are analyzed.For the basic model of deep learning,the principles of each model are described from three aspects: structure,feature learning and reverse parameter optimization.The research method of the RUL prediction for the shearer key parts based on the deep learning is put forward.(2)For the full life cycle data,a model is built based on the concept of classification to predict the RUL.According to the monitoring means and the characteristics of the monitoring data,the 3 sigma criterion denoising method is introduced to remove the gross errors in the monitoring data.In order to ensure the integrity of the data,the method of stratified sampling is applied to obtain the training set and test set.Then,a deep convolutional neural network(DCNN)prediction model without pooling layers is constructed to improve the features learning ability of model.The experimental results show that the proposed model has the advantages of high predictability and strong generalization.(3)For the non-full life cycle data,an RUL prediction model is built based on the concept of regression.Fast Fourier transform(FFT)is applied to obtain the frequency doamin information.The input data features of time domain and frequency domain are get by self supervised learning of auto-encoder(AE).The bidirectional gated recurrent unit(bi-GRU)prediction model is constructed by adding forward layers in the hidden layer of gated recurrent unit(GRU),which realizes the bidirectional learning of data features,the extracted features by AE are regarded as the input of prediction model,driving bi-GRU to predict the RUL.It is verified by experiments that the model has the ability of accurate prediction.(4)The two proposed prediction models in this paper are compared from structures,data preprocessing and prediction results.Combined with the actual situation of coal mine,the system framework of RUL prediction scheme for shearer key parts based on deep learning is constructed.Then,the proposed deep learning models are embedded into the data analysis layer of the Internet of things(Io T)to realize the RUL prediction.The data transmission strategy of shearer monitoring is studied.Taking the gears and bearings in the high-speed and lowspeed areas of the vulnerable parts of the shearer rocker arm as examples,the feasibility of the two models in predicting the RUL of the vulnerable parts of the rocker arm is discussed in this paper.The method of RUL prediction for the shearer key parts based on the deep learning is used to train the deep neural network by the mechanical monitoring signals.The advantage of deep learning is that it can get rid of the dependence on a large number of signal processing technology and prediction experience,overcome the shortcomings of traditional prediction methods,complete the adaptive extraction of features,realize self-learning and dynamic prediction,improve the prediction accuracy of the prediction results and the intelligent level of the analysis means,and provide guidance for the effective implementation of the predictive maintenance strategy for the shearer.