# Machinery Fault Signal Detection with Deep One-Class Classification
## 4 Experimental Study
### Experimental Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Methods**} & \multicolumn{4}{c}{**CWRU Dataset**} & \multicolumn{4}{c}{**PU Dataset**} & \multicolumn{4}{c}{**Rolling Element Platform**} \\  & \multicolumn{4}{c}{**(Case 1)**} & \multicolumn{4}{c}{**(Case 2)**} & \multicolumn{4}{c}{**(Case 3)**} \\ \cline{2-11}  & **Scenario** & **Scenario** & **Scenario** & **Scenario** & **Scenario** & **Scenario** & **Scenario** & **Vibration** & **Acoustic** \\  & **1** & **2** & **3** & **4** & **1** & **2** & **3** & **4** & **Signals** & **Acoustic** \\ \hline PCA + KDE & **1.000** & 0.986 & 0.992 & 0.993 & 0.340 & 0.790 & 0.780 & 0.827 & 0.665 & 0.603 \\  & **(0.000)** & (0.005) & (0.005) & (0.001) & (0.006) & (0.005) & (0.005) & (0.004) & (0.005) & (0.009) \\ \hline PCA + IF & 0.995 & 0.994 & 0.994 & 0.994 & 0.349 & 0.782 & 0.778 & 0.822 & 0.722 & 0.618 \\  & (0.002) & (0.000) & (0.000) & (0.000) & (0.036) & (0.007) & (0.013) & (0.004) & (0.005) & (0.004) \\ \hline PCA + SVDD & **1.000** & 0.994 & 0.994 & 0.994 & 0.393 & 0.791 & 0.776 & 0.825 & 0.735 & 0.637 \\  & **(0.000)** & (0.000) & (0.000) & (0.000) & (0.101) & (0.005) & (0.017) & (0.004) & (0.003) & (0.005) \\ \hline AE + KDE & 0.883 & 0.548 & 0.524 & 0.391 & 0.681 & 0.730 & 0.733 & 0.748 & 0.519 & 0.534 \\  & (0.172) & (0.047) & (0.031) & (0.054) & (0.015) & (0.036) & (0.024) & (0.023) & (0.113) & (0.023) \\ \hline AE + IF & 0.870 & 0.084 & 0.092 & 0.122 & 0.707 & 0.748 & 0.749 & 0.746 & 0.264 & 0.551 \\  & (0.168) & (0.097) & (0.102) & (0.044) & (0.009) & (0.012) & (0.013) & (0.008) & (0.124) & (0.025) \\ \hline AE + SVDD & 0.858 & 0.286 & 0.530 & 0.535 & 0.717 & 0.684 & 0.650 & 0.700 & 0.412 & 0.530 \\  & (0.159) & (0.118) & (0.085) & (0.084) & (0.011) & (0.057) & (0.082) & (0.042) & (0.165) & (0.038) \\ \hline CAE + KDE & 0.978 & 0.993 & 0.993 & 0.993 & 0.987 & 0.847 & 0.869 & 0.867 & 0.921 & 0.801 \\  & (0.028) & (0.001) & (0.001) & (0.002) & (0.001) & (0.054) & (0.015) & (0.054) & (0.009) & (0.042) \\ \hline CAE + IsoForest & 0.962 & 0.998 & 0.988 & 0.974 & 0.980 & 0.860 & 0.883 & 0.870 & 0.924 & 0.819 \\  & (0.072) & (0.036) & (0.036) & (0.072) & (0.002) & (0.046) & (0.026) & (0.061) & (0.009) & (0.061) \\ \hline CAE + SVDD & 0.976 & 0.985 & 0.999 & 0.974 & 0.980 & 0.877 & 0.895 & 0.882 & 0.794 & 0.622 \\  & (0.047) & (0.042) & (0.001) & (0.072) & (0.004) & (0.038) & (0.024) & (0.056) & (0.048) & (0.070) \\ \hline ReSAE & 0.992 & 0.996 & 0.996 & 0.994 & 0.068 & 0.347 & 0.320 & 0.358 & 0.809 & 0.569 \\  & (0.001) & (0.002) & (0.003) & (0.000) & (0.011) & (0.022) & (0.017) & (0.024) & (0.038) & (0.021) \\ \hline ReCAE & 0.999 & 0.999 & 0.998 & 0.998 & 0.048 & 0.327 & 0.303 & 0.333 & 0.732 & 0.569 \\  & (0.002) & (0.001) & (0.002) & (0.001) & (0.001) & (0.003) & (0.002) & (0.003) & (0.002) & (0.007) \\ \hline AnoGAN & 0.765 & 0.567 & 0.570 & 0.604 & 0.216 & 0.452 & 0.399 & 0.417 & 0.722 & 0.691 \\  & (0.024) & (0.036) & (0.037) & (0.095) & (0.116) & (0.139) & (0.101) & (0.207) & (0.014) & (0.042) \\ \hline Soft-boundary & 0.980 & 0.994 & 0.985 & 0.989 & 0.792 & 0.823 & 0.760 & 0.791 & 0.819 & 0.643 \\ deep SVDD & (0.005) & (0.003) & (0.002) & (0.003) & (0.007) & (0.007) & (0.004) & (0.007) & (0.079) & (0.091) \\ (AE pre-trained) & (0.005) & (0.003) & (0.003) & (0.003) & (0.004) & (0.005) & (0.007) & (0.003) & (0.006) & (0.103) & (0.028) \\ \hline Soft-boundary & & & & & & & & & & & \\ deep SVDD & 0.985 & **1.000** & **1.000** & **1.000** & **0.991** & 0.936 & 0.941 & **0.940** & **0.985** & 0.912 \\ (AE pre-trained; & (0.026) & **(0.000)** & **(0.000)** & **(0.000)** & **(0.001)** & **(0.007)** & **(0.008)** & **(0.007)** & **(0.002)** & **(0.011)** \\ \hline One-class deep & & & & & & & & & & & \\ SVDD & **(0.000)** & **1.000** & **1.000** & **1.000** & **0.991** & **0.939** & **0.944** & 0.939 & **0.985** & **0.922** \\ (AE pre-trained; & (**0.000)** & **(0.000)** & **(0.000)** & **(0.000)Table 7 shows that the traditional OCC methods with independently performed feature extraction yielded lower AUROC values than deep neural network-based OCC methods.