## 3 Experimental Study
### Experimental Setup
#### 3.1.9 Prognostic Procedure  
The overall framework proposed for the RUL prediction of turbofan engines using target label construction based on change point detection and feature engineering is presented in Figure 15. Within the framework, data preprocessing is the first critical step in reducing the complexity of calculations required for RUL prediction. Feature selection was performed by selecting sensors whose values changed over time to reduce the complexity. Since sensors have measurement values in different units, min-max normalization was performed per sensor to ensure comparability and eliminate bias. Filtering was applied to uncover the main trend in sensor values due to noise in the dataset. Feature extraction was achieved using an autoencoder-based dimensionality reduction approach to create an encoded signal from the selected sensor data. Also, the encoded signal was used to create the target label value based on the change point on this single signal. A new feature was constructed from the dataset and added to the existing dataset to enhance the prediction accuracy.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Hyperparameters** & **CNN** & **LSTM** & **CNN-LSTM** \\ \hline Number of layers & 1 & 1 & 1 \\ Number of epochs & 50 & 50 & 30 \\ Batch size & 128 & 64 & 256 \\ Filter size in the first layer & 32 & - & 32 \\ Number of units in the first layer & - & 128 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Selected hyperparameter values.  
Figure 15: The prognostic procedure of the proposed model.  
The target RUL labeling is a crucial factor that affects the prediction accuracy in supervised learning. Since predefined target labels did not exist in the dataset, the PwL degradation models, which are commonly used in the literature, were also adopted here. Although there were multiple sensor signals for each engine in the dataset, a one-dimensional encoded signal was obtained using an autoencoder network to determine a single change point for each engine. Two PwL functions were fitted to the encoded signal using a convex optimization algorithm, yielding a breakpoint where the signal decreased linearly after its breakpoint value. After determining the breakpoints, target RUL labels specific to each motor were created. The final input data sample preparation was conducted using the sliding window technique after all the data preprocessing steps. The input data was finally shaped to a size of \(30\times 2\), which consisted of a fused sensor signal and the new difference feature for each time step of the sliding window.  
The RUL prediction was performed using the 1D-CNN-LSTM hybrid network and compared separately with the base 1D-CNN, base LSTM networks, and other traditional machine learning methods. In the training phase, samples prepared using sliding window methods were used in the k-fold cross-validation approach to find the best neural network hyperparameters. Predictions were made for all combinations of hyperparameter value ranges using the main effect analysis in the training phase. The hyperparameters yielding the best results in the cross-validation were used in the test phase for the RUL prediction.