# Fault Diagnosis of Planetary Gear Train Crack Based on DC-DRSN
## 1 Introduction
[9] proposed a mixed-domain fault diagnosis method for chaotic particle swarm with improved cross-mutation, which effectively improved the accuracy of bearing fault classification based on a mixed eigendomain. [PERSON] et al. [10] proposed a fault diagnosis method based on the combination of integrated empirical mode decomposition and a support vector machine for diesel engine vibration signal analysis and feature extraction, and verified that the method can accurately and effectively identify multiple sources of fault information of a gearbox in different states through comparative experiments. However, traditional machine learning methods are limited to shallow feature representation and struggle to deal with complex nonlinear relationships, which leads to low accuracy in fault diagnosis. In recent years, with the development of artificial intelligence and big data technologies, the methods such as deep learning as well as transfer learning have become widely used in the field of fault diagnosis [11; 12; 13]. [PERSON] et al. [14] proposed an adaptive neural network model based on instantaneous speed information to realize the fault detection of a planetary gearbox under different working conditions. [PERSON] et al. [15] proposed a diagnostic method of a parallel multi-scale pooled branch convolutional neural network, which solved the fault diagnosis problem of mechanical vibration signals under multiple working conditions with less computational cost. However, with the increase in network layers and training data, problems such as network degradation and overfitting have become increasingly prominent, which greatly affects the accuracy of fault diagnosis. Therefore, [PERSON] et al. [16] proposed to use a \"shortcut connection\" deep residual network (ResNet) to solve the degradation phenomenon and training problem of the deep network, which optimized the convergence speed and achieved the improvement of accuracy by solving the degradation problem during deep network processing. [PERSON] et al. [17] proposed a deep residual shrinkage network (DRSN) based on the residual network, which was used to improve the feature learning ability of strong noise vibration signals. [PERSON] et al. [18] combined variational mode Gaussian distortion with DRSN to improve the fault diagnosis performance. [PERSON] [19] proposed a state-of-health identification method for rotating machinery based on Gramian angle field (GAF) and DRSN, which was used to identify faults in rotating machinery under the condition of nonlinear transformation speed and load. [PERSON] et al. [20] converted the one-dimensional logging parameters into two-dimensional images by using GAF, input them into a DRSN model, and experimentally proved that the diagnostic accuracy of the proposed method was better than that of the traditional DRSN and convolutional neural network (CNN). [PERSON] et al. [21] proposed a DRSN method based on characteristic noise energy ratio (CNER), which showed that the average recognition rate could reach 98.70% in the environment with 10 dB SNR. [PERSON] et al. [22] proposed the Pareto-optimal adaptive loss residual shrinkage network (PALRSN), which effectively solves the problem of mechanical fault diagnosis when the fault sample data are insufficient. In summary, the existing fault diagnosis methods have achieved certain results in low-noise environments, and the diagnostic accuracy is high. However, in strong-noise environments, the diagnostic performance and accuracy of the models need to be improved. Considering that the gearbox of a nuclear power unit works in the environment of strong noise and low SNR, a fault diagnosis method based on DC-DRSN is proposed to improve the fault diagnosis performance of the model. The main contributions of this paper are as follows: (1) The structure of one-dimensional and two-dimensional parallel residual shrinkage networks is constructed to enhance the feature extraction capability.