# Whitening CNN-Based Rotor System Fault Diagnosis Model Features
## 3 Proposed Improvement for Fault Diagnosis Models
### Model Architectures  
[PERSON]'s model [32] was one of the first CNN-based models proposed for vibration-based fault diagnosis of a rotating system. The original study showed that the model could accurately detect bearing faults from induction motor currents. The original model architecture consists of three CNN layers (Conv1D) and two fully-connected layers (FCL). The original model was slightly modified for the experiments in this study. Table 1 details the modified model architecture. Furthermore, Figure 2 shows the corresponding feature maps between the model layers. The modified version of [PERSON]'s model only employs one fully connected layer. The output space spans over 10 probabilities for every bearing health state in the CWRU dataset. In addition, the output space can include only one value corresponding to the probability of a fault, if the model is optimised for the binary thruster fault detection tasks. Furthermore, BN was placed after every CNN layer, despite BN not being mentioned in the original publication proposing [PERSON]'s model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Kernel Size** & **Channels in** & **Filters** & **Stride** & **Padding** \\ \hline Conv1D & \(9\times 1\) & 1 or 2 & 60 & 5 & 18 \\ Conv1D & \(9\times 1\) & 60 & 40 & 5 & 4 \\ Conv1D & \(9\times 1\) & 40 & 40 & 9 & 3 \\ FCL & 1\(\times\)1 & 400 & 1 or 10 & N/A & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: [PERSON]’s model architecture. Figure 2: Feature maps of the [PERSON]’s model employed in this study. These feature maps correspond to a model that diagnoses the probability of 10 health states from vibration data measured with two vibration sensors. WDCNN is an accurate model for vibration based condition monitoring [15]. The model consists of two levels. The first level includes five 1D-CNN, BN and pooling layers employed for feature extraction. The second level includes two fully connected layers with BN for fault recognition. Table 2 details the WDCNN architecture. Furthermore, Figure 3 shows the corresponding feature maps between the model layers. The first layer filters consist of wider 64 \(\times\) 1 kernels. Each filter includes a kernel for every input channel. The rest of the CNN layers consist of 3 \(\times\) 1 kernels. After every CNN layer, there is a BN layer, max-pooling layer and a (ReLU) activation function, in this order. All max-pooling layers apply 2 \(\times\) 1 kernel with a stride of 2. The final fully connected layers compute the probabilities for the system health states. SRDCNN [18] is a promising model, which achieved relatively high accuracies in bearing fault diagnosis tests in the original study. Similar to [PERSON]'s model and WDCNN, SRDCNN extracts features with 1D convolutional layers and then computes the probabilities for the system health with fully-connected layers. Table 3 lists the main components of SRDCNN architecture. Furthermore, Figure 4 shows the corresponding feature maps between the model layers. The model consists of five convolutional layers and two fully-connected layers. However, the convolutional layers of SRDCNN differ from the convolutional layers of WDCNN and [PERSON]'s model. SRDCNN applies dilated convolutions. Furthermore, each convolutional layer includes two adjacent convolutional sublayers and a residual connection. These adjacent sublayers are structured similarly to the input gates in the recurrent neural network type known as long short-term memory (LSTM). The activation values of these sublayers are multiplied element-wise together and then added element-wise to the residual values. The residual values are the input values to the layer passed through a third adjacent 1D-convolutional sublayer with \(1\times 1\) kernels. These dilated convolutional layers