## 4 Experimental Results
### Training the CAE Model  
As mentioned above, power spectrum images of the normal health stage were used to train the CAE model. Before training the CAE model, some parameters needed to be configured such as the number of layers, number of nodes in each layer, activation function for each layer, iteration number, and the learning rate. Regarding the number of layers and the number of nodes in hidden layers, the parameters are related to the dimension of the input data. These parameters are usually obtained experimentally and provide a good visual conformity. The convolutional autoencoder model starts with propagating from the input layer to the convolution layer. The convolutional encoder consists of three convolutional layers, each followed by a max pooling layer. The number of filters for the three convolutional layers were (1,64,128), and the filter size for all layers was \(3\times 3\). Features map from the convolutional encoder section were flattened and passed through the autoencoder layers. The autoencoder section consisted of three fully connected layers with 64, 2, and 64 neurons in each layer. Therefore, the input 2D RGB image was transformed into a two-dimensional feature space in the bottleneck layer that was used to construct the _HI_. To transform the extracted features back to the original image, we added three deconvolutional layers with three upsampling layers in between to the autoencoder section. The number of filters for the three deconvolutional layers were [1,64,128], with the filter size of \(3\times 3\).  
To attain better visual conformity, after several trials, we chose the SELU as the activation function for the convolutional and deconvolutional layers, ReLU for the autoencoder layers, and the sigmoid for the last layer. To illustrate the influence of the learning rate on training the model, we show the reconstruction error under various learning rates in. If the learning rate was too low, the convergence was too slow or overfitting; if the learning rate was too high, it would hinder the convergence. Therefore, the learning rate was set to 0.005 for this study. As illustrated in Figure 8, the reconstruction error did not significantly decrease after 30 cycles; therefore, 30 iterations were performed in this study. The experimental model was developed using the Python-based Keras library [46] with a TensorFlow backend [47].  
Since the CAE model is trained in an unsupervised manner, the successful trained CAE should learn to extract meaningful features from the images of the normal operation, and to reconstruct a closely similar image to the original image in the output. However, perfect reconstruction is usually a sign of overfitting where it only learns to copy its input to the output without learning to extract intelligent features and generalize to a new instance. Indeed, reasonably close reconstruction with a small error demonstrates that the CAE learned the meaningful features of the training dataset and had an acceptable generalization. Figure 9 represents the comparison of the original images with the reconstructed images by the developed model. Although the CAE model was solely trained by the  
Figure 8: The reconstruction error of deep learning model for different learning rates.  
normal operation dataset, it also reconstructed faulty images very well, indicating that the model could extract subtle features from images.