## 1 Introduction  
The planetary gearbox is widely used in wind turbines, modern vehicles, ships and warships, helicopters, and many other applications. As such gearboxes are fundamental for power transfer, they are often mission critical and safety critical, so ensuring stable and safe operation is vitally important for their production and life. As a crucial component of the gearbox, the planetary bearing is more subject to the external environment, coupling between structures, loads, and working conditions than other structural components. Thus, the vibration signal patterns related to the bearings are more complicated, which makes its health monitoring and diagnostics more difficult. Early damage detection is a key diagnostics requirement for ensuring safe high-precision equipment operation, eventually enabling predictive maintenance. However, the features that characterize early damage are relatively weak and hidden by large external interference. In addition, big amounts of data will be generated during industrial operation, so a great challenge is to propose novel diagnostics tools to substitute the traditional signal processing-based methods which may be impractical for the planetary gearbox bearing fault diagnostics. In fact, there is an urgent need for a high-efficiency and high-precision algorithm for batch data processing robust to external interference. Artificial neural networks [1], support vector machines [2], and deep learning [3] are three of the most representative intelligent fault diagnosis methods which were used by many scholars to realize batch processing of vibration data. It is found that the traditional neural network algorithm underperformed in terms of structural robustness, adaptability,and accuracy for weak signals. A support Vector Machine (SVM) is limited in processing high-dimensional multi-class data. The deep learning method has become a popular algorithm with its excellent performance, which has won the favor of researchers. The greatest advantage of deep learning is that there is no need to manually select features during operation. Deep learning algorithms automatically learn how to extract the target information of the original data layer by layer through multiple nonlinear connection layers enabling the extraction of complex features. The existing deep learning network structures include: the Convolutional Deep Neural Network (CNN) [4; 5; 6], Deep Belief Network (DBN) [7], Recurrent Neural Network (RNN) [8], Sparse Autoencoder (SAE) [9], etc. It is widely applied in image processing and pattern recognition. Among the deep learning algorithms, CNN was the first deep learning network structure to be proposed. It has attracted the attention of researchers in many fields and has achieved remarkable results, such as bearing fault diagnosis. [PERSON] et al. [10] proposed a new convolution theory based on feature fusion which can improve the adaptive ability of the algorithm in different feature scale-spaces. [PERSON] et al. [11] proposed a method to optimize the parameters of the deep CNN model using a particle group algorithm, which can remove the influence of prior knowledge on fault diagnosis results. [PERSON] et al. [12] normalized the data and gave different weights to the loss function with different distribution ratios to weaken the class shift of the sample recognition results in the case of data imbalance. [PERSON] et al. [13] applied a multi-head attention to optimize the CNN structure, which improved the accuracy of identification. Although these methods led to good diagnostics results, issues related to poor diagnostic performance for weak fault signals with low signal-to-noise ratios and small relative amplitudes remain unresolved. The fault diagnosis method based on deep learning requires massive learning datasets to accurately reveal the internal structure and characteristic behavior of the analyzed signals.