## 4 Experimental Results
### Application
#### 4.1.1 Time Window  
The first inspected parameter is the time window of the input samples. More specifically, the time window defines dataset segmentation into smaller samples reflecting the operational profile over the specific timeframe, dictating the number of samples each input time series batch comprises, along with the final number of available samples to train and assess the model. Indicatively, Figure 12 illustrates the division of a random signal into 10 segments. In this regard, it is investigated whether having fewer but wider input time series results in more effective models or vice versa. Figure 13 tabulates the instances with the different inspected time intervals of the input samples, keeping constant the degradation degree induced; information regarding the degradation is presented in the next paragraph. First, data segmentation, using an overlap of 90% between adjacent samples, according to the 8 h window results in 10,205 input time series with a length of 475 data points, the 1-day time window leads to the generation of 3930 batches of time series of 1440 data points, and lastly, using the 3-day window results in the creation of 1280 time series samples, each containing 4320 time stamps. It should be noted that the overlap percentage was implemented to generate more samples. Figure 12: Demonstration of segmentation. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter Name** & **Values Audited** \\ \hline Number of conv. layers & 2, 3, 4 \\ Number of kernel per layer & 6, 8, 10, 12, 15 \\ Kernel size & 2, 3, 5, 7 \\ Number of Fully Connected (FC) layers & 1, 2 \\ Number of nodes per FC layer & 3, 5, 7 \\ Learning rate & 10\({}^{-3}\), 7.5\(\cdot\)10\({}^{-4}\), 5.10\({}^{-4}\), 2.5\(\cdot\)10\({}^{-4}\), 10\({}^{-4}\), 7.5\(\cdot\)10\({}^{-5}\), 5:10\({}^{-5}\), 2.5\(\cdot\)10\({}^{-5}\), 10\({}^{-5}\) \\ Batch size & 64, 128, 256, 512,1024 \\ Epochs & 50, 100, 150, 200, 300, 400, 500 \\ Dropout rate & 0, 0.1, 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameter inspected values. Figure 13: Time interval varying models. Moreover, it is very significant to ensure that the dataset contains observations reflecting equally each of the seven possible operational states, so that the dataset is not skewed, thus ensuring that the model is trained equivalently upon all possible conditions. For that reason, the original dataset corresponding to the well-functioning vessel is concatenated with six other similar datasets, with the only difference based in the fact that the artificial damage inducement is undertaken evenly across all the cylinders, ensuring that all seven conditions are equivalently represented in the final dataset. Therefore, comparing with what was explained above, the final number of time series samples generated from each time window was multiplied by seven: the 8 h, 1-day, and 3-day time frames comprised 71,435, 27,510, and 8,960 input time series batches, respectively. Lastly, the training, validation, and test subsets are defined. The incentive of the training set consists of the time series dedicated for algorithm learning, hence, requiring the majority of the available samples. The validation set is utilized as means of tracking the progress of the algorithm during training, and the test set consists of unseen samples, to examine the ability of our models to generalize and return the desired results. Applying a 70%-10%-20% percentage distribution in each instance, which is frequently seen in the machine and deep learning community, the samples each of the inspected time windows provide are listed  
by the high metric scores, as shown in Table 5. It should be again emphasized that in a seven-class classification problem, each metric score occurs seven times, for each label, and in Table 6 the averages of these seven values are presented for each metric.