# Machinery Fault Signal Detection with Deep One-Class Classification
## 4 Experimental Study
### Experimental Results
In addition, the null hypothesis of performance equivalence was rejected with a significance level of \(\alpha=0.01\), and it implies that there is a significant difference in performance between the CAE pre-trained deep SVDD (proposed) and other methods. Consequently, these results denote that the proposed deep SVDD with CAE pre-training significantly outperformed the others. Finally, there is no statistical difference between the two deep SVDD methods pre-trained by CAE, which are not significant. In spite of their equivalent performance, we suggest using the one-class deep SVDD because it alleviates the difficulty of selecting the hyperparameter \(\
u\) in the soft-boundary deep SVDD method. In this study, we also conducted additional experimental studies to examine the effect of a number of latent features on the fault signal detection performance by varying the number of latent features from 16 to 256. The AUROC values over different numbers of latent features in all cases are presented in Figure 9.  
As shown in Figure 9, the fault signal detection performance of the deep SVDD model rarely differs by changing the number of latent features. Therefore, we specified the number of latent features as 16 in order to prevent the overfitting problem and mitigate the computational burden of the deep SVDD model. Finally, in order to demonstrate the advantage of the deep SVDD method regarding fault signal detection, we compared it with other feature extraction methods (PCA, AE, and one-dimensional CAE). To this end, we visualized these 16 latent features extracted from these four methods by facilitating the t-distributed stochastic neighborhood embedding  
Figure 9: AUROC values over different numbers of latent features in (**a**) the CWRU dataset (Case 1), (**b**) the PU dataset (Case 2), and (**c**) the Rolling element experiment platform dataset (Case 3). (t-SNE [58]) technique. Figure 10 shows the two-dimensional t-SNE plots of latent features obtained from four feature extraction methods. As shown in Figure 10, in the latent features obtained from PCA and AE, normal and fault signals are quite overlapped each other. The PCA cannot deal with the non-linear patterns, and thus, it might not properly deal with the complex time series signal data. Moreover, in the AE, the layers are fully connected to each other, and these fully connected structures also cannot accommodate the temporal properties of the time series signals. For these reasons, these two feature extraction methods cannot accurately detect the fault signals. Conversely, the one-dimensional CAE and deep SVDD pre-trained by one-dimensional CAE more clearly discriminate the fault signals from normal ones than PCA and AE. These results indicate that one-dimensional CAE can more properly handle the time series signals because the convolutional blocks (i.e., convolutional-max pooling layer pair) in CAE help to consider the temporal properties of the time series signals [51; 52]. On the other hand, latent features obtained from the deep SVDD more clearly separate the fault signals and normal signals than those from one-dimensional CAE. In the fine-tuning procedure of the deep SVDD, latent features are updated to be specialized for fault signal detection. Therefore, the deep SVDD method can generate more useful features for fault signal detection tasks than only using one-dimensional CAE. Figure 10: _Cont._  
Figure 10: Two-dimensional t-SNE plot of 16 latent features obtained from PCA, AE, CAE, and deep SVDD in (**a**) Scenario 1 of the CWRU dataset (Case 1), (**b**) Scenario 1 of the PU dataset (Case 2), and (**c**) vibration signals of Rolling element experiment platform dataset (Case 3).