# Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
## 4 Experimental Study
### Experimental Parameter Setting  
Based on the MBCNN-BiLSTM model, this paper designs the following parameters: a five-layer convolutional network, a one-layer BiLSTM network, a one-layer GAM network and two fully connected layers, and the last layer is the output layer. In addition, a BatchNormalization (BN) layer and a random deactivation (dropout) function are added to the network to prevent overfitting and enhance network robustness. Hyperparameters are used to define the structure of the neural network; however, there is no consensus on how to set these hyperparameters. This paper sets these based on the existing popular recommendations [18; 21; 23]. The detailed parameters of the model are provided in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Structure Name** & **Parameter** & **Activation Function** \\ \hline Convolutional layer 1 & (16, 64, 16) (16, 64, 8) & ReLu \\ Convolutional layer 2 & (16, 3, 1) (16, 3, 1) & ReLu \\ Convolutional layer 3 & (32, 3, 1) (23, 3, 1) & ReLu \\ Convolutional layer 4 & (64, 3, 1) (64, 3, 1) & ReLu \\ Convolutional layer 5 & (64, 3, 1) (64, 3, 1) & ReLu \\ Fully connected layer 1 & 128 & ReLu \\ Connet & - & - \\ BiLSTM layer & Hidden size 16 & tanh \\ GAM layer & - & - \\ Fully connected layer 2 & 100 & ReLu \\ Output layer & 1 & - \\ \hline \hline \end{tabular}  
* The table (16, 64, 16) indicates that a convolution kernel has a number of 16, a convolution kernel size of 64 and a step size of 16.  
\end{table}
Table 2: MBCNN-ABiLSTM model parameters.  
Figure 9: Vibration signals from bearing dataset.  
Since the bearing life prediction problem is a regression problem, MSE is chosen to be a loss function for model training; it is given by the following equation:  
\[\text{MSE}=\frac{1}{N}\sum_{i=1}^{N}\left[f(i)-\hat{f}(i)\right]^{2} \tag{11}\]  
where \(N\) denotes the count of samples, \(f(i)\) denotes the true RUL value that in the \(i\)-th sample, \(\hat{f}(i)\) denotes the prediction value of RUL that in the \(i\)-th sample.  
For this paper, the model is trained for 50 rounds per experiment with a training batch size of 256. If the time window is too small, the prediction performance of the model is low. If the time window is too large, too much information is input to the model, which will increase the model training time. In order to maintain a balance between prediction performance and computational configuration, the time window size is set to 16. Since the Adam algorithm is currently the dominant neural network optimization algorithm, it is chosen for the network parameter optimization algorithm in this paper, and its initial learning rate is chosen to be 0.01. In addition, the computers for the experiments were configured as follows:  
* The CPU is [EMAIL_ADDRESS] GHz with 32 GB of memory;
* The GPU is NVIDIA Quadro P2200 with 5 GB of video memory;
* The operating system is Windows 10;
* The programming language is Python 3.10, which is implemented on the basis of the pytorch2.0 deep learning framework, and the experiments use the GPU to accelerate the speed of training.