## 4 Experimental Assessment
### DL-Based Diagnostic Assessments
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Spindle Only**} & \multicolumn{2}{c}{**Frame Only**} & \multicolumn{3}{c}{**Spindle + Frame**} \\ \hline
**Parameter** & \(SP_{R}\) & \(SP_{C}\) & \(FR_{R}\) & \(FR_{C}\) & \(SP_{-}FR_{R}\) & \(SP_{-}FR_{C}\) & \(SP_{R}FR_{C}\) & \(SP_{C}FR_{R}\) \\ \hline
**Dimension** & \((m\times n)\) & \((m\times n)\) & \((m\times n)\) & \((m\times n)\) & \((m\times n)\) & \((m\times n)\) & \((m\times n)\) \\  & \(\times\) 4 & \(\times\) 4 & \(\times\) 3 & \(\times\) 3 & \(\times\) 7 & \(\times\) 7 & \(\times\) 7 & \(\times\) 7 \\ \(CNN64\) & 90.2\% & 97.8\% & 85.4\% & 88.9\% & 92.1\% & 98.0\% & 84.2\% & 88.3\% \\ \(CNN64\_64\) & 92.1\% & 97.6\% & 88.1\% & 90.0\% & 92.5\% & 98.7\% & 88.9\% & 94.7\% \\ \(CNN64\_Dense100\) & 93.6\% & 98.2\% & 88.6\% & 91.2\% & 92.2\% & 97.8\% & 90.1\% & 95.1\% \\ FCN & 96.0\% & 99.3\% & 91.1\% & 95.5\% & 95.7\% & 98.8\% & 92.3\% & 97.6\% \\ DNN64 & 73.8\% & 85.6\% & 56.2\% & 75.8\% & 65.8\% & 87.2\% & 74.7\% & 83.7\% \\ DNN128\_64 & 88.7\% & 91.6\% & 82.6\% & 88.7\% & 90.3\% & 93.1\% & 90.6\% & 94.2\% \\ DNN100\_150\_50 & 89.1\% & 92.1\% & 84.0\% & 90.1\% & 91.2\% & 94.0\% & 92.4\% & 93.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test accuracy of DL models on different input/source data channels. As shown in the maximum true positives in Figure 10d, it can be observed that the FCN is the most accurate of all the models, followed by the \(CNN64\_Dense100\) in Figure 10c, whose worst prediction was recorded for the \(FR_{R}\) data source. As the CNN models become more shallow (\(CNN64\) and \(CNN64\_64\)), the false alarm rate of predictions becomes higher, as observed in the false positives and false negatives in Figure 10a,b. Although the best outputs for the \(CNN64\) and \(CNN64\_64\) models are perfect, such as the FCN, the reduced  
Figure 10: Worst and best Confusion matrices produced by the DL models (**a**) \(CNN64\), (**b**) \(CNN64\_64\), (**c**) \(CNN64\_Dense100\), (**d**) FCN, (**e**) DNN64, (**f**) DNN128\(\_64\), and (**g**) DNN100\(\_150\_50\). true positives in their worst outputs, as shown in Figure 10a,b, only reflect on the inherent deficiencies of shallow CNN models, especially for inputs with very small discriminator in the inputs, such as the \(FR_{R}\). In contrast, the \(SP_{C}\), \(FR_{C}\), \(SP_{-}FR_{C}\), which, respectively, are critical segments from the raw signals, offer inputs that are more discriminative for improved diagnostic results. Overall, most of the best outputs presented in Figure 10 were produced for the inputs \(SP_{C}\), \(FR_{C}\), \(SP_{-}FR_{C}\), while the worst outputs were produced for \(FR_{R}\), which generally hints not only at the importance of the spectral isolation but also the limited efficiencies of monitoring the component via the frame only. While the sensors under the spindle provide more resourceful information for monitoring, the sensors on the frame provide supplementary information for a more comprehensive monitoring process and are recommended (even though the overall accuracy of using the spindle only is marginally higher). Although the results in Table 2 and Figure 10 support the use of the FCN model for accurate monitoring, it is also important to assess the computational cost implications of using the models. This offers another avenue for a more informed decision-making criteria for diagnostic modeling. In view of this, we conducted a computational cost assessment on the models, and it is presented in Figure 11.  
The values shown in Figure 11 above are the times (in minutes) for the training-testing process of each DL model on the respective input/source data channels. The hardware specifications for the computer used in the analyses is: AMD Ryzen 7 (manufactured in Taiwan), 2700 Eight-core 3.20 GHz processor, and 16 GB RAM, while the analyses were performed using Keras--a Python-based DL library. As shown, the computational costs of using the FCN are at least three to four times higher than using the other models.