## 4 Experimental Results
### Application
#### 4.1.2 Induced Degradation  
Subsequently, various degrees of induced degradation were also audited, attempting to determine how the efficacy of the proposed networks varied regarding the level of degeneration we aspired to detect. In other words, we examined whether our models could \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1** \\ \hline
1\% jitter factor & 100 & 100 & 100 & 100 & 100 \\
2\% jitter factor & 99.8 & 99.9 & 99.9 & 100 & 99.9 \\
5\% jitter factor & 96.6 & 96.6 & 96.9 & 98.6 & 96.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Metrics scores across the three developed instances with a degradation input window. Figure 15: Distribution of accuracy scores of the numerous models during hyperparameter tuning across the three developed instances. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1** \\ \hline
8 Hours & 100 & 100 & 100 & 100 & 100 \\
1 Day & 99.8 & 99.9 & 99.9 & 100 & 99.9 \\
3 Days & 96.6 & 96.6 & 96.9 & 98.6 & 96.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Metric scores across the three developed instances with a variable input window. successfully identify even subtle deficiencies contributing to timely recognition of failure proneness. For our study, having available data representing a defective condition of the vessel was essential for training the network. Nevertheless, the data collected through the LAROST(tm) CMS reflected a well-functioning state of the ship with no signs of degradation. This obstacle was overcome by artificially inducing degradation of the ship by trying to imitate its behavior at the degraded condition intended for detection. More explicitly, white noise was convolved with the original signal of the designated cylinder, which would manifest defective patterns. Regarding the values of the white noise distribution:  
* The mean value \((\mu)\) of the white noise signal is equal to the respective value of the well-functioning signal, namely the value that it would have if we did not intervene. We denote this as I.S (Initial Signal), the vector of the parameter being altered. * Its standard deviation (s) is set equal to the multiple of the standard deviation of the healthy signal of the respective feature. We denote this as STD\({}_{\text{LS}}\) (Standard Deviation of the Initial Signal), the standard deviation of the initial signal. In other words, the white noise being convolved is governed by a distribution  
\[\text{W.N.}\ \sim\ \mathcal{N}(\text{LS., k}*\text{STD}_{\text{LS.}}),\]  
where k is the factor being multiplied with the standard deviation, which causes the inflation of the initial signal, as illustrated in Figure 16. The inspected values of the inflation factor are 1%, 2%, and 5%. Figure 16 shows the instances created with variable jittering factors. In Figure 17, the signal of the well-functioning vessel is shown along with the three levels of induced degradation. The yellow-colored signal represents the inflated jitter with the 1% factor and is not easily noticed from the healthy signal with mere observation. The 2% degeneration inducement, shown in darker orange, is slightly easier to distinguish, whereas the signal with the 5% degeneration, shown in red, is significantly more volatile, thus easily noticed. Following the previous pattern, the various models defined by the different hyper-parameter configurations, at each instance with the diverse degree of degradation, are ranked according to their efficacy and the ROC curves of the models appertaining to the 20 th, 50 th, 80 th, and 100 th (optimal) percentile are plotted, as illustrated in Figure 18. Additionally, the histogram shown in Figure 19 illustrates the distribution of the average accuracy achieved, among the seven classes and across the various audited configurations for the three instances that were developed.