## 4 Results
### Overall Model Performance  
The results displayed in Tables 5 and 6 show the performance of the models for every different experiment. However, to obtain a better understanding in general, we calculated the mean accuracy score across all experiments for both datasets to give an indication of the overall performance, regardless of the experiment combinations related to train splits and machine conditions. In addition, we describe the standard deviation to explain the stability of the models' performance, therefore making some assumptions regarding the generalizability of the models. Furthermore, for the CNNs, the model size in terms of parameters and computation speed based on GPU acceleration for every epoch is described in Table 7.  
As can be seen in Table 8, the proposed adaptive models clearly outperform any other model. However, it seems that it depends on the properties of the dataset which model performs best. For example, the _A-WMCNN_ performs most optimal on the CWRU dataset, also with the second lowest standard deviation, while having a lower performance on the Paderborn dataset, with a relatively high standard deviation. Vice versa, we see similar behavior for the _A-WCNN_ model. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Experiment Combination** & **Number of Sequences** & **Training Data (\%)** & \multicolumn{5}{c}{**Accuracy**} \\ \hline  & & **K-NN** & **RF** & **SVM** & **WDCNN** & **A-WCNN** & **A-WMCNN** & **Ada-WMCNN** \\ \hline
**Real/Healthy** & 200.000 & 80 & 97.34 & 98.25 & 90.14 & 99.24 & **99.96** & 98.99 & 99.67 \\  & & 60 & 97.09 & 98.00 & 90.02 & 99.31 & **99.94** & 98.99 & 99.71 \\  & & 20 & 95.72 & 97.20 & 89.49 & 98.75 & **99.75** & 98.56 & 99.55 \\  & & 10 & 94.44 & 96.50 & 88.90 & 97.84 & 99.61 & 95.78 & 99.06 \\  & & 5 & 92.40 & 95.39 & 87.82 & 97.23 & **99.03** & 93.70 & 98.10 \\ \hline
**Real/Artificial** & 259.875 & 80 & 90.61 & 93.01 & 77.18 & 97.53 & **99.80** & 96.58 & 99.45 \\  & & 60 & 89.91 & 92.43 & 77.10 & 97.05 & **99.74** & 96.18 & 99.35 \\  & & 20 & 86.14 & 90.21 & 76.53 & 96.75 & **99.36** & 92.82 & 98.56 \\  & & 10 & 83.34 & 88.53 & 75.79 & 95.99 & **98.82** & 91.42 & 97.39 \\  & & 5 & 79.50 & 86.71 & 74.50 & 93.36 & **97.60** & 87.85 & 97.21 \\ \hline
**Healthy/Artificial** & 179.875 & 80 & 89.19 & 92.28 & 77.50 & 97.01 & **99.63** & 95.41 & 98.55 \\  & & 60 & 88.37 & 91.67 & 77.68 & 96.20 & **99.69** & 98.04 & 98.96 \\  & & 20 & 85.21 & 89.68 & 77.15 & 96.30 & **99.20** & 91.33 & 98.06 \\  & & 10 & 82.52 & 87.92 & 76.81 & 93.92 & **98.46** & 90.29 & 97.51 \\  & & 5 & 78.71 & 85.90 & 76.58 & 91.50 & 95.87 & 90.08 & 93.86 \\ \hline
**Real/Artificial/Healthy** & 319.875 & 80 & 90.64 & 93.16 & 78.17 & 94.63 & **99.47** & 92.17 & 98.75 \\  & & 60 & 89.98 & 92.69 & 78.20 & 95.77 & **99.36** & 94.61 & 98.59 \\  & & 20 & 86.85 & 90.78 & 77.80 & 93.96 & **99.15** & 95.26 & 98.18 \\  & & 10 & 83.98 & 89.25 & 77.00 & 92.02 & **98.63** & 88.49 & 96.76 \\  & & 5 & 80.58 & 87.14 & 75.70 & 74.41 & **96.98** & 84.16 & 95.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Test accuracy scores (%) averaged across the 5-fold cross validation for each of the models with their respective train split on the Paderborn Bearing dataset. Another interesting observation is the performance of the optimized multichannel model (_Ada-WMCNN_). While performing a bit worse than the best performing model in both experiments, its averaged overall performance is better. Further, as Table 7 suggests, this model has less parameters and a competitive computation speed compared to the other adaptive CNNs. Therefore, according to this study, the _Ada-WMCNN_ is well-suited for a variety of fault detection and condition monitoring tasks. Finally, it is worth mentioning that even though the ML models perform considerably worse than the deep learning approaches, the random forest (RF) algorithm seems to mark the highest performance with a fairly low standard deviation, indicating that this ML model is surprisingly stable under varying circumstances.