## 4 Experiment Study
### Results and Discussion for RUL Prediction
#### 4.3.1 Results and Discussion for Initial Degradation Detection  
(1) Results and Discussion  
During initial degradation detection, we set the former 20% of the target bearing life data for the training of DACAEN, and the whole-life data is input into the trained model for calculating the RAPP health indicator. The results of RAPP aiming at all bearings are listed in Figure 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Name** & **Module Name** & **Network Layer** & **Activation Function** & **Stride** & **Size** & **Number** & **Output Size** \\ \hline \multirow{9}{*}{DACAEN} & \multirow{6}{*}{Generator} & Convolution layer1 + BN & Leaky relu & \(1\times 16\) & \(1\times 64\) & 16 & \(16\times 80\) \\  & & Convolution layer2 + BN & Leaky relu & \(1\times 4\) & \(1\times 4\) & 32 & \(32\times 20\) \\  & & Convolution layer3 + BN & Leaky relu & \(1\times 4\) & \(1\times 4\) & 64 & \(64\times 5\) \\  & & Average Pooling layer & - & - & - & - & \(1\times 64\) \\  & & Hidden layer & - & - & - & - & \(1\times 1\) \\  & & FC layer & - & - & - & - & \(1\times 320\) \\  & & Translation layer & - & - & - & - & \(64\times 5\) \\  & & Deconvolution layer3 + BN & Leaky relu & \(1\times 4\) & \(1\times 4\) & 32 & \(32\times 20\) \\  & & Deconvolution layer3 + BN & Leaky relu & \(1\times 4\) & \(1\times 4\) & 16 & \(16\times 80\) \\  & & Reconstruction Layer3 + BN & Leaky relu & \(1\times 16\) & \(1\times 4\) & 1 & \(1\times 1280\) \\ \hline \multirow{9}{*}{Discriminator} & \multirow{6}{*}{Discriminator} & Convolution layer1 + BN & Leaky relu & \(1\times 16\) & \(1\times 64\) & 16 & \(16\times 80\) \\  & & Convolution layer2 + BN & Leaky relu & \(1\times 4\) & \(1\times 4\) & 32 & \(32\times 20\) \\  & & Convolution layer3 + BN & Leaky relu & \(1\times 4\) & \(1\times 4\) & 64 & \(64\times 5\) \\  & & Average Pooling Layer & - & - & & \(1\times 64\) \\  & & FC layer & Softmax & - & \(1\times 1\) & 1 & \(1\times 1\) \\ \hline \multirow{9}{*}{JTMN} & \multirow{6}{*}{Feature extractor} & Convolution layer1 + BN & Leaky relu & \(1\times 10\) & \(1\times 64\) & 16 & \(16\times 128\) \\  & & Pooling layer & - & \(1\times 4\) & \(1\times 4\) & 16 & \(16\times 32\) \\ \cline{1-1}  & & Convolution layer2 + BN & Leaky relu & \(1\times 1\) & \(1\times 4\) & 32 & \(32\times 32\) \\ \cline{1-1}  & & Pooling layer & - & \(1\times 4\) & \(1\times 4\) & 32 & \(32\times 8\) \\ \cline{1-1}  & & Convolution layer3 + BN & Leaky relu & \(1\times 1\) & \(1\times 4\) & 64 & \(64\times 8\) \\ \cline{1-1}  & & Pooling layer & - & \(1\times 4\) & \(1\times 4\) & 64 & \(64\times 2\) \\ \cline{1-1} \cline{2-6}  & Domain & Flatten layer & - & & - & - & \(1\times 128\) \\ \cline{1-1} adaptation & FC layer 1 & Sigmoid & - & - & 64 & \(1\times 64\) \\ \cline{1-1} and & FC layer 2 & Sigmoid & - & - & 1 & \(1\times 1\) \\ \cline{1-1} regressor Classifier & Gradient reversal layer & - & - & - & - & - \\ \cline{1-1}  & FC layer 1 & Softmax & - & - & 2 & \(1\times 2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed experimental parameters of the proposed DACAEN. Figure 4: _Cont_. After construction of the RAPP, according to the former 20% of the target bearing life data, the health threshold of the tested bearing is obtained based on 3\(\sigma\) criteria. Once the RAPP exceeds the health threshold, IDD will be determined. In this paper, five consecutive RAPP values exceeding the health threshold are recorded as early degradation points. The results of IDD are listed in Table 3.  
Figure 4: RAPP health indicator of IEEE PHM bearings. (2) Ablation Study  
Compared to traditional reconstruction error methods, the advantage of RAPP is attributed to the adversarial mechanism strategy of DACAEN. To demonstrate this advantage, the reconstruction ability of DACAEN and DCAE are compared. The details are shown in Table 4.  
Take Bearing 1-1 as an example.