# Fault Diagnosis of Planetary Gear Train Crack Based on DC-DRSN
## 3 Fault Diagnosis Method Based on DC-DRSN and Verification
### Method Validation  
#### 3.2.1 Introduction of Dataset  
At present, there is no authoritative report on the public dataset of planetary gearboxes, so the bearing public dataset of Case Western Reserve University (CWRU) in the United States is used to verify the fault diagnosis method proposed in this paper. The rolling bearing test bench of CWRU is shown in Figure 4, which is mainly composed of a motor, a torque sensor, a power tester, and an electronic controller. During the test, the motor speed is 1720 r/min-1797 r/min. The test bearings are 6205-2 RS JEM SKF deep groove ball bearings with single-point damage machined by EDM, which is made in Sweden. The load of the bearings has 1 hp and 3 hp loads two types, and the sampling frequency of the acceleration sensor is 48 kHz. The bearing conditions involved in the dataset include four types: normal, inner ring damage, outer ring damage, and rolling element damage. Each type is available in 0.007\" (0.1778 mm), and 0.021\" (0.5334 mm) damage diameters. \begin{table}
\begin{tabular}{c c c} \hline
**No. of Layer** & **Layer** & **Hyper-Parameters** \\ \hline Conv1\_1 & Convolution layer1\_1 & 32 convolution kernels with the size of [3,3]. \\ BN & Batch Normalization layer & \
\begin{tabular}{c} Stride: [1,1]. Padding: same. \\ The scale is 32. \\ \end{tabular} \\ Conv2\_1 & Convolution layer2\_1 & 32 convolution kernels with the size of [3,3]. \\ BN & Batch Normalization layer &
\begin{tabular}{c} Stride: [1,1]. Padding: same. \\ The scale is 32. \\ \end{tabular} \\ Average Pooling & Average Pooling layer & Pooling size: [1,1]. Stride: [2,2]. Padding: [0,0,0,0]. \\ \hline \end{tabular}
\end{table} 
Table 1: DRSN hyperparameter settings. Table 2 shows the dataset used in this paper, which contains nine fault states. The number of samples in each category is 900, and the number of data points in a single sample is 1024, for a total of 8100 samples. The dataset is randomly divided into training sets, validation sets and test sets in the ratio of 3:1:1. Thus, there are 540 training sets, 180 validation sets and 180 test sets for each fault type. The Fourier time-frequency diagram of size 112 \(\times\) 112 is input in the upper channel, so the structure of the training set, the verification set and the test set of each fault type are 540 \(\times\) 112 \(\times\) 112, 180 \(\times\) 112 \(\times\) 112 and 180 \(\times\) 112 \(\times\) 112, respectively. While the structure of the training set, the verification set and the test set of each fault type in the lower channel are 540 \(\times\) 1024, 180 \(\times\) 1024 and 180 \(\times\) 1024, respectively. In order to accelerate the convergence of the model and improve the stability and robustness of the model, all samples are normalized and preprocessed. In this paper, TensorFlow and Keras frameworks are used to build the DC-DRSN network, with a learning rate of 0.001, a batch size of 64, and a training times of 150, in which the convolutional layer adopts the normal distribution initialization method and L2 regularization proposed in Ref. [10]. Table 3 shows the images obtained by using STFT, WT, GASE, and GST to convert the vibration signals in the nine different states of the common dataset. It can be seen that the time-frequency diagram converted by GASE and GST cannot clearly show the characteristics of the signal. The time-frequency diagram of STFT is clearer than that of WT and can clearly separate and analyze different frequency components. Therefore, in this paper, STFT is selected for time-frequency analysis of the fault signal to be diagnosed.