# Machinery Fault Signal Detection with Deep One-Class Classification
## 4 Experimental Study
### Experimental Results
Those results indicate that separate feature extraction procedures might not generate specialized features for fault signal detection tasks. Conversely, the deep neural network-based OCC methods generally perform better than traditional OCC methods owing to their end-to-end learning manners for the fault signal detection tasks. In the deep neural network structures, latent features are optimized for fault signal detection tasks in that these features are simultaneously generated to minimize the loss functions for the fault signal detection tasks. Therefore, it confirms that deep neural network-based methods are more appropriate for detecting fault signals than traditional OCC methods with separate feature extraction procedures. However, in the AnoGAN model, the generator part may poorly generate artificial target signals due to the mode collapse problem, and it results in undesirable fault signal detection performance. Besides, the reconstruction-based methods, ReSAE and ReCAE, also cannot perform better than the deep SVDD models because the reconstruction error of the input signal might not directly quantify the anomalous level of the input signal. Hence, the weights of the stacked AE or CAE should be updated for more accurate fault signal detection tasks. Conversely, both one-class deep SVDD and soft boundary deep SVDD models outperform other deep neural network-based methods because the more specialized features of fault signal detection can be derived as minimizing loss functions for anomaly detection tasks. It should be noted that the pre-training with CAE performs better than those with stacked AE because convolutional layers of the CAE network help to draw temporal information of time-series signals because they combine the signal values within adjacent time intervals. Therefore, the initial neural network structures of the encoding part in CAE can accommodate the intrinsic properties of time-series signal data, and it eventually helps to build more accurate decision boundaries for detecting fault signals. In addition, we conducted a post-analysis on performance differences through a nonparametric statistical method. We applied the Wilcoxon signed rank test [57] regarding the statistical difference in AUROC values among all the above 16 methods. Based on \(p\)-values over all methods, the null hypothesis of performance equivalence between CAE pre-trained one-class deep SVDD (the top-ranked method) and other ones is tested. The results of the Wilcoxon signed rank test are provided in Table 8. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Average Rank**} & \multirow{2}{*}{\(p\)**-Value**} & \multirow{2}{*}{
\begin{tabular}{c} **Hypothesis** \\ **(\(\alpha\) = 0.01)** \\ \end{tabular} } \\ \hline One-class deep SVDD (CAE pre-trained; & \(1.1\) & - & - \\ Proposed) & & & \\ Soft-boundary deep SVDD (CAE pre-trained; & \(1.9\) & \(0.1056\) & Not reject \\ Proposed & \(5.9\) & \(0.0059\) & Reject \\ CAE + KDE & \(6.0\) & \(0.0058\) & Reject \\ CAE + SVDD & \(6.7\) & \(0.0020\) & Reject \\ PCA + SVDD & \(6.8\) & \(0.0089\) & Reject \\ Soft-boundary deep SVDD (AE pre-trained) & \(7.8\) & \(0.0020\) & Reject \\ PCA + IF & \(7.9\) & \(0.0058\) & Reject \\ One-class deep SVDD (AE pre-trained) & \(8.2\) & \(0.0020\) & Reject \\ PCA + KDE & \(8.6\) & \(0.0092\) & Reject \\ ReSAE & \(9.9\) & \(0.0059\) & Reject \\ ReCAE & \(10.0\) & \(0.0059\) & Reject \\ AnoGAN & \(12.7\) & \(0.0020\) & Reject \\ AE + KDE & \(13.1\) & \(0.0020\) & Reject \\ AE + IF & \(13.5\) & \(0.0020\) & Reject \\ AE + SVDD & \(13.6\) & \(0.0020\) & Reject \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Wilcoxon signed-rank test results. As shown in Table 8, both the CAE pre-trained one-class deep SVDD and soft-boundary deep SVDD methods attained the best or second average rank.