Fault Diagnosis Of Key Parts For Shearer Based On Deep Learning

As an important mechanical equipment in coal mining,shearer has complex structure,high reliability,high power and easy maintenance.Among them,the safe and stable operation of shearer equipment is of great significance to ensure the mining efficiency of coal.However,because the shearer is in a bad working environment with serious electromagnetic interference and humidity for a long time,some key parts often fail.For example,in the shearer rocker transmission system,the gear is easy to wear,break and crack.However,the occurrence of these failures is likely to lead to the decline of production efficiency and even casualties.Therefore,the fault diagnosis of the key parts of the shearer is particularly important.At present,the traditional fault diagnosis methods such as time domain analysis,frequency domain analysis and BP neural network are often used to identify the faults of the key parts of the shearer.However,traditional fault diagnosis methods are mostly realized by small-scale data analysis.In the face of a large number of signals with multiple working conditions alternation and unknown fault information,there are some problems such as one-sided analysis results,poor accuracy,low efficiency and lagging intelligence.In view of the above problems,this paper takes the high speed gear of the shearer rocker arm cutting part as the research object,and adopts the deep learning algorithm model of one-dimensional convolution neural network(1D-CNN)and pre-activation deep residual network(PA-DRN)to study the gearing fault diagnosis of the shearer rocker arm.The main research contents include the following three aspects:(1)1D-CNN fault diagnosis model suitable for the original time domain signal is constructed based on the basic theory of convolutional neural network in deep learning,The structure of the model is optimized by dropout strategy and batch normalization(BN).The proposed 1D-CNN model is validated by CWRU bearing data.Firstly,the vibration signal of bearing is processed by data enhancement,and it is divided into training set,validation set and test set.Then,the 1D-CNN model is used to train,verify and test the one-dimensional vibration signal samples of bearing.By comparing with deep neural network(DNN),sparse autoencoder(SAE)and deep belief network(DBN)models,the results show that the comprehensive recognition rate of the 1D-CNN model on the bearing is 100%.(2)The 1D-CNN model is applied to the gearing fault classification of the shearer rocker arm.Firstly,the loading test-bed of the shearer rocker arm and the acceleration sensor are used to simulate the different faults of the gears,and the vibration signals are collected.Then,1D-CNN model is used to analyze the influence of different training parameters of the model on the gearing classification effect of rocker arm.Finally,the gearing classification performance of 1D-CNN model on the shearer rocker arm is analyzed by t-SNE and evaluation method based on confusion matrix.The experimental results show that the 1DCNN model effectively improves the gearing classification accuracy of the shearer rocker arm,which reaches 98.60%.(3)The PA-DRN fault diagnosis model is constructed by optimizing the residual learning module.On the one hand,the PA-DRN model is validated by CWRU bearing data,and the effectiveness of PA-DRN model is proved by confusion matrix visualization and generalization performance validation experiments.On the other hand,the PA-DRN fault diagnosis model is applied to the gearing fault identification of the shearer rocker arm.Through the evaluation method of confusion matrix,the visualization of classification process and the comparative analysis of models,it is proved that PA-DRN model has a good classification effect on the gearing fault identification of shearer rocker arm,and its classification accuracy reaches 99.07%.