## 3 Experimenting Results
### Diagnosis  
After performing anomaly detection, isolating different failure types and diagnosing their root cause is the next stage of predictive maintenance. For this stage, clustering, visualization, projection, and XAI techniques were used in the 2D-CNN-AE anomaly detection model of previous section given this achieved the best results overall. The first experiment required forward passing through the encoder of original test data and synthetic failure data generated from test data, which turned each stroke into a 32-dimensional feature vector. Its objective was to test the ability to differentiate failure types in this compressed space. Initially, a 2-component _t_-SNE to feature vectors was applied with the objective to create a 2D space where clusters are visualized. The result of this experiment with 32-dimensional feature vector where disperse clusters given that the space might be too big or noisy for _t_-SNE to find clear relations. Therefore, experimenting with dimensionality reduction techniques was performed while keeping data variability to facilitate _t_-SNE. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{3}{c}{**(1) Force Increase**} & \multicolumn{3}{c}{**(2) Force Anticipation**} & \multicolumn{3}{c}{**(3) Power Increase**} & \multicolumn{3}{c}{**Switch Forces**} \\ \hline  & **10\%** & **25\%** & **50\%** & **100\%** & **120-170\%** & **70-170\%** & **10\%** & **25\%** & **50\%** & **100\%** & \\ \hline PCA cycle & 0.27 & 0.74 & **0.97** & **0.97** & 0.90 & 0.93 & 0.10 & 0.16 & 0.65 & **0.97** & **0.97** \\ PCA feats & 0.67 & 0.67 & 0.67 & 0.67 & 0.67 & 0.67 & 0.02 & 0.09 & 0.62 & 0.67 & 0.67 \\ ELM cycle & 0.17 & 0.21 & 0.24 & 0.25 & 0.18 & 0.21 & 0.15 & 0.15 & 0.15 & 0.16 & 0.23 \\ ELM feats & 0.07 & 0.22 & 0.68 & 0.76 & 0.23 & 0.52 & 0.06 & 0.06 & 0.07 & 0.07 & **0.91** \\ OC-SVM cycle & 0.67 & 0.68 & 0.68 & 0.68 & 0.66 & 0.66 & 0.66 & 0.66 & 0.66 & 0.66 & 0.68 \\ OC-SVM feats & 0.41 & 0.67 & 0.67 & 0.67 & 0.47 & 0.67 & 0.08 & 0.06 & 0.04 & 0.02 & 0.67 \\
**Null space** & 0.82 & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & 0.47 & **1.00** & **1.00** & **1.00** & **1.00** \\
**CNN-AE 900** & **0.91** & **0.91** & **0.91** & **0.91** & **0.91** & **0.91** & **0.91** & **0.91** & **0.91** & **0.91** & **0.91** \\
**CNN-AE 95** & **0.99** & **0.99** & **0.99** & **0.99** & **0.99** & **0.99** & **0.99** & **0.99** & **0.99** & **0.99** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: F1 score of anomaly detection models trained under the assumption that at least 90% of training data is correct. CNN-AE’s p90 and p95 refer to the threshold used for anomaly detection, indicating percentile 90 and percentile 95 of correct validation data, respectively. Similar to the original \(t\)-SNE work [31], PCA has been used for dimensionality reduction to speed up computation and suppress noise without distorting pairwise distance, with the objective to facilitate \(t\)-SNE's work and analyze whether clearer clusters are created. PCA requires standardization of features, and 6 components were selected to keep 95% of initial variability. After PCA, these features were inputted to \(t\)-SNE that was configured with 2 components, 10 learning rate, 10,000 maximum iterations, and remaining parameters as default for sklearn. A grid search on perplexity was performed, which is a parameter that represents number of nearest neighbors in manifold learning algorithms, so values from where 5-70 range in strides of 5, and range 70-200 in strides of 10 were tested. The results showed three groups of data where differentiation between increase forces and increase power consumption failures was difficult given that failures with smallest data variation (10%) were very near in the new space. The experiment was repeated, and then performed clustering using gaussian mixture models (GMM) with 4 number of clusters, with the objective to isolate the 4 failure types in \(t\)-SNE's embedded space of two dimensions.