Research On Fault Diagnosis Of Hoisting Bearing Based On Deep Neural Network

Mine hoist is a key equipment widely used in the coal mine industry.Its stability and good safety conditions are important guarantees for the coal mine production and life of underground workers.Rolling bearings are the core components of the hoisting equipment,and their operating status directly affects the efficiency and safety of the hoisting equipment.How to carry out efficient and accurate diagnosis of bearing components and then realize intelligent maintenance of equipment are several urgent problems to be solved in the current large-scale coal mine production.First,aiming at the instability of artificially extracted features and data scarcity in traditional diagnostic methods,this paper proposes a double-layer fault diagnosis algorithm based on deep neural network.This algorithm can perform accurate fault detection on noise-free bearing vibration data.Firstly,the original data set is expanded by the sliding window overlap sampling technique,and then,self-autoencoder technology is used to extract the features of the data.Subsequently,using the extracted features as the input of the double-layer neural network,the back-propagation algorithm is used to train the fault diagnosis classifier.Finally,the specific classification of the fault is decided by voting based on the ensemble learning.In this paper,the bearing public data set of Case Western Reserve University is used to simulate bearing fault diagnosis in an ideal and noise-free environment to verify the effectiveness of the proposed algorithm.The proposed algorithm is compared with the classic support vector machine and back propagation neural network algorithm.Experimental results show that the average accuracy of the proposed algorithm for fault diagnosis is about 99.95%,which is significantly higher than comparison algorithms.Second,aiming at the problems of large actual noise of industrial data and variable load of mechanical operation,this paper proposes a double-layer neural network fault diagnosis algorithm based on adaptive batch normalization.The proposed algorithm integrates the adaptive batch normalization method with the noise-free fault diagnosis algorithm,and the dropout layer is added to improve the generalization ability of the network.In the training phase,the neural network model is trained with back-propagation algorithm.Then,the scaling variables and translation variables of all batch normalization layers are recorded.In the diagnosis phase,if the test set and the training set have different domain distributions,the mean and variance of the test set are calculated to replace the training mean and variance of each batch normalization layer in the trained model.We perform the normalization to the result,and then scaled and translated to achieve the effect of domain adaptation.The proposed method is validated for its fault diagnosis performance under different noise,variable load,and mixed noise-to-load conditions.Experimental results show that the proposed method has high noise immunity,variable load capacity and accuracy of fault diagnosis.In summary,this paper studies the noise-free fault diagnosis of hoist bearings and the fault diagnosis problems of noisy and variable load,and respectively proposes a double-layer fault diagnosis algorithm based on deep neural network and a double-layer neural network fault diagnosis algorithm based on adaptive batch normalization.The experimental results show that the proposed algorithm can excellently perform the fault diagnosis task of hoist bearing under complex working environment conditions,which has certain guidance for fault diagnosis and intelligent production of coal mine.There are 21 pictures,10 tables and 111 references in this paper.