# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 1 Introduction
They straightforwardly input the original one-dimensional vibration signal into the network for feature extraction and classification, and good results were obtained. [PERSON] et al. [13] put forward a ResNet network model to settle a matter of gradient disappearance and gradient explosion in deep neural network training by introducing residual blocks. [PERSON] et al. [14] proposed an adaptive transfer learning fine-tuning method to find the best fine-tuning strategy for each instance and obtained relatively high accuracy on the vision 10-item dataset. Lately, in order to realize better fault diagnosis results in the field of fault diagnosis, the approach of extracting vibration signal features from high-dimensional scales has gradually been adopted [15]. Among various fault diagnosis methods, many transform one-dimensional signals into two-dimensional feature maps [16]. For example, the Gramian angular field (GAF) [17] was first adopted to transform the original time series into two-dimensional feature maps with unique features. Then, the data containing rich feature information were combined with CNN, ResNet, DenseNet [18] MobileNet [19], VGGNet [20] and other models to achieve ideal diagnosis results. The signals collected in the actual working conditions are often not ideal, and the mechanical operating environment is often more complex, especially when the passenger flow is concentrated and the use environment is more complex, such as high-speed rail, railway stations, airports, overpasses, tunnels and transportation hubs. The signal collected by the sensor contains much noise and is a nonstationary, nonlinear random signal. In fault diagnosis, noise interferes with fault extraction. Therefore, to extract accurate and comprehensive fault characteristic data and realize accurate fault diagnosis, the interference of noise must be reduced as much as possible. To solve the above matters, this paper put forward a fault diagnosis method of gearbox based on an ICEEMDAN-MPE-adaptive wavelet threshold (ICEEMDAN-MPE-AWT) and SE-ResNeXt50 transfer learning models. The principal innovations of this paper are described below:ICEEMDAN was used to self-adaptively decompose noisy signals so as to extract effective information in the signals, which can better deal with aliasing and pseudo-mode problems that may exist in the signal decomposition process. At the same time, by calculating the MPE of each IMF, the noise-dominated and signal-dominated components were effectively screened, improving the accuracy of fault diagnosis. 2. The AWT noise reduction method was adopted. On the basis of introducing a correction factor to dynamically adjust the threshold, the convergence factor \(\theta=k/L\) was used to further adjust the calculation results of the threshold; thus, the noise reduction effect was improved. 3. The Gramian angular sum field (GASF) was used to convert one-dimensional data into unique two-dimensional feature images, and the SE ResNeXt50 transfer learning model was proposed by combining fine-tuned transfer learning, ResNeXt and SEnet, which improved the generalization ability and accuracy of the model and ensured the precision of gearbox fault diagnosis. The remainder of this paper is structured as follows. The ICEEMDAN-MPE-AWT noise reduction method is introduced in Section 2. Section 3 introduces the Gramian angular field. The SE-ResNeXt50 transfer learning model is proposed in Section 4. Section 5 carries out the fault diagnosis experiment and analyzes the experimental results. Some conclusions of this paper are drawn in Section 6.