Research On Fault Diagnosis Method Of Planetary Gearboxes Based On Semi-Gragh Convolution

Planetary gearbox is widely used in precision transmission and heavy machinery due to its compact structure,which can achieve high transmission ratio and high power deceleration motion.When the internal structure of the planetary gear box fails,it will seriously affect the stable operation and safe work of the equipment system,and will cause significant property losses and casualties in serious cases.Therefore,it is of great significance to study the fault diagnosis method of planetary gearbox and identify the fault type accurately and quickly for the safe operation of equipment.This thesis takes planetary gearbox as the research object,aiming at the key problems in the field of fault diagnosis,including signal noise reduction,feature dimension reduction and fault diagnosis.Firstly,aiming at the limited adaptive denoising ability of sparse representation signal denoising algorithm and the lack of theoretical interpretation of deep learning signal denoising algorithm,an autoencoder signal denoising method based on KSingular Value Decomposition(K-SVD)is proposed.According to the morphological characteristics of vibration signals of planetary gearboxes,Fourier atoms are used to construct the initial dictionary.The network structure of K-SVD denoising method was improved,and the self-encoder of K-SVD was built to improve the denoising ability.Batch denoising is applied to the input data set to improve computational efficiency.The influence of network layer number and initialization dictionary mode on k-SVD autoencoder was investigated by inputting the simulation signal of planetary gear local fault to denoise.The effectiveness and superiority of the proposed algorithm are proved by comparing the noise reduction effect of orthogonal matching pursuit and K-SVD method.Secondly,as the traditional eigendimension reduction methods do not fully mine the information between data,the Laplace Eigenmapping(LE)is used for eigendimension reduction.The time-domain and frequency-domain characteristics of k-SVD autoencoder after denoising were extracted.By constructing the objective function of Laplacian feature mapping,the eigenmatrix after dimension reduction is obtained.Explore the influence of Kernel parameters on LE dimension reduction method and compare it with principal Component Analysis(PCA)and Kernel Principal Component Analysis(KPCA)dimension reduction methods.The effectiveness and superiority of LE dimension reduction method used in this paper are proved.Thirdly,a semi-supervised Graph Convolutional neural Network based on Convolutional neural Network is proposed to solve the problem that current fault diagnosis methods require a great deal of supervised learning with fault label data,and the acquisition of fault labels costs a great deal of manpower and material resources.Semi-GCN planetary gearbox fault diagnosis method.After dimension reduction of LE feature,undirected graph was constructed by Euclidean distance method,semiGCN layer unit was constructed according to spectral theory,and semi-GCN semisupervised fault diagnosis model was established.The influence of semi-GCN data label rate and convolution kernel size on semi-CGN is discussed.Compared with BP neural network and convolutional neural network fault diagnosis models,the effectiveness and superiority of semi-GCN is proved.Finally,in order to verify the validity of the proposed method for actual data,a planetary gearbox fault diagnosis test rig was built,and the k-SVD autoencoder noise reduction method,LE feature dimension reduction method and semi-GCN fault diagnosis method were experimentally verified,which proved the validity and the Superiority of the proposed method.