## 3 Experimenting Results
### Diagnosis
Low values of perplexity increased data sparsity in the new space, whereas high values increased cluster compactness. However, beyond a certain perplexity value, data distribution did not change much and clusters remained stable, so this point was selected for final \(t\)-SNE results analysis. This point was achieved with perplexity of 145, which is shown in Figure 5: it contains real failure labels on the left, whereas results of GMM with 4 number of clusters are presented in the right. This \(t\)-SNE diagnosis has clearly differentiated the four clusters, except for several increase forces (10%, 25%, and 50%) strokes that were assigned to power consumption failure. Moreover, several increases in power consumption (10%, 25%, and 50%) were assigned to the increase forces failure cluster. The smallest magnitudes of these two failure classes are still too close to the healthy data for the clustering algorithm to differentiate between them. This technique has the advantage of enabling results visualization. However, given its difficulty for hyperparameter tuning without the information of number of clusters and failure labels, it can be hard to implement with semi-supervised models. Another disadvantage of \(t\)-SNE is its possibility to create non-existent patterns on data given its adaptation to it. To complement \(t\)-SNE experiments, another clustering algorithm was applied to latent space data after PCA, aiming at creating clusters that separate different failure types automatically. The OPTICS density clustering algorithm was selected for this task, and the minimum number of samples parameter from 20 to 140 was grid searched to find the configuration where the algorithm detected 4 clusters. The min num samples parameter that created 4 clusters is 80, and its results are presented in Table 5. Moreover, the algorithm creates a group for outlier samples, which do not belong to any discovered clusters according to their distance in the feature space. Figure 5: \(2\)\(t\)-SNE space images, containing failure labels in the left part, and GMM clustering labels in the right part. According to Table 5, the failure switch forces is correctly isolated given the cluster 4 has a precision and recall of 1 for this failure type. Cluster 3 contains only instances of force increase failure, but not all of them are gathered given the recall is lower than 1; the remaining instances are assigned several to cluster 1 and the rest to the outliers group. Similarly, cluster 2 contains only force anticipation failure data, and the other instances are assigned to outliers group. In addition, cluster 1 contains mainly power increase failure data, but it also contains some force increase instances, being an overlap of two failures; the remaining power increase instances are assigned to the outliers group. Finally, the outliers group gathers an important number of instances belonging to force increase, force anticipation, and power increase failure types, but none of switch forces failure type. All in all, there is a small overlap between force increase and power increase failure types. In addition, many force increase, force anticipation, and power increase instances are assigned to outliers group. At this point, the results of clustering with lower min samples was analyzed, which generates more clusters. This showed that lower magnitude versions of each failure type had higher probability to be assigned together than higher magnitude ones, which were correctly separated. To continue diagnosis analysis, correct stroke data and the versions of highest magnitude for each failure type were projected to latent space using 2D-CNN-AE: 100% force increase, 100\({}^{\circ}\) force anticipation, 100% power increase and switch forces. Projected feature vectors were afterwards z-scaled, and normalized in [0,1] range to have all variables in a comparable distribution and range.