# Machinery Fault Signal Detection with Deep One-Class Classification
## 4 Experimental Study
### Experimental Settings
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \
\begin{tabular}{c} **The Number of** \\ **Target Signals** \\ **(Normal Signals)** \\ \end{tabular} &
\begin{tabular}{c} **The Number of** \\ **All Fault Signals** \\ \end{tabular} \\ \hline Scenario 1 & & \\ Scenario 2 & & \\ Scenario 3 & & \\ Scenario 4 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The number of target and fault signals in PD benchmark signal datasets. Figure 5: Example segmented signals of all bearing conditions in Scenario 1 of PU datasets. Figure 6: Rolling element experimental platform. Similar to the CWRU dataset, the signals are collected from normal, inner race fault, outer race fault, and ball element fault conditions because these three faults are the most representative fault types [54; 55]. These three faults in the bearing were generated by an electrical discharge machine. Furthermore, we also considered two fault intensity levels (i.e., strong and weak) by different defect diameters for each fault type because the signal patterns and amplitude scales differ from fault intensities. The examples of the vibration and acoustic signals collected from various bearing conditions are presented in Figure 7 and Figure 8, respectively. The number of signals collected from both normal and all fault conditions is presented in Table 5.  
The number of target and fault signals obtained from our own rolling element experiment platform. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Signal Types**} & \
\begin{tabular}{c} **The Number of** \\ **Target Signals** \\ **(Normal Signals)** \\ \end{tabular} &
\begin{tabular}{c} **The Number of** \\ **All Fault Signals** \\ \end{tabular} \\ \hline Vibration & 4850 & 21,050 \\ Acoustic & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The number of target and fault signals obtained from our own rolling element experiment platform. Figure 7: Example of vibration signals obtained from our own rolling element experiment platform. In this study, we considered the traditional OCC methods as follows: SVDD, KDE, and IsoForest, which are the most representative boundary-based, probability distribution-based, and ensemble-based methods. For these traditional OCC methods, additional feature extraction procedures should be performed beforehand. To this end, we used three feature learning methods, principal component analysis (PCA), stacked autoencoder (stacked AE), and convolutional autoencoder (CAE). Then, we applied the SVDD, KDE, and IsoForest to the latent features obtained from those three methods. For the SVDD, we used the radius basis function (RBF) kernel having a parameter \(\gamma\). In addition to \(\gamma\), the traditional SVDD model requires other regularization hyperparameter \(C\). In this study, we explored the hyperparameter pair \((\gamma,C)\) within the given range: \(\gamma\in\{2^{-10},2^{-9},\ldots,2^{-1}\}\) and \(C\in\{0.01,0.02,0.03,\ldots,0.1\}\), respectively. For KDE, we also used a Gaussian kernel as the kernel function having bandwidth \(h\), and the hyperparameter \(h\) was chosen within the range of \(h\in\{2^{0.5},2^{1},\ldots,2^{5}\}\). Finally, for the IsoForest, we empirically selected the number of isolation trees, \(B\), within the range of \(B\in\{50,100,200,500,1000\}\). For these traditional OCC methods, we chose the optimal hyperparameter achieving the best performance within these candidate sets. The hyperparameter optimization for the OCC methods is not easy tasks because, as mentioned earlier, the fault detection is basically unsupervised problem settings [36; 37]. In other words, in these problem settings, the label information is not available. To the best of our knowledge, there is lack of systematic ways to optimize the hyperparameters in fault detection problems. Hence, as in previous studies [35; 56], we also picked the hyperparameters showing the highest AUROC values. We also considered the following deep OCC methods for fault signal detection: reconstruction-based, GAN-based, and boundary-based methods.