改进时序灰度图和深度学习的齿轮箱故障诊断OA北大核心CSTPCD

A gear box fault diagnosis method based on improved time series gray image and deep learning is put forward in order to solve the problems such as complex actual working environment of gear box,inadequate performance of extracting features by traditional methods and gray image extraction features.After the vibration signal is decomposed into several intrinsic mode components(IMFs)by EEMD,the IMFs are divided into high-frequency and low-frequency components by cumulative mean criterion,in which the high-frequency components are denoised by wavelet threshold.The high frequency IMFs and low frequency IMFs after noise reduction are reconstructed,and the reconstructed signal is coded by gray image method.Two-dimensional improved time series gray image is sent to convolution neural network for training,so as to exert the advantages of convolution network in feature extraction of pictures and display the results by confusion matrix.Finally,the model results and different gray scale images are compared with traditional diagnosis methods.The results show that compared with common gray image and global denoising gray image,the method proposed in this paper improves the accuracy of gear box fault diagnosis by 4 and 1.8 percentage points respectively,and the convergence speed is significantly faster.Compared with BP neural network and ELM diagnosis method,the method proposed in this paper significantly improves the accuracy of gear box fault diagnosis.