## 3 Experimenting Results
### Adaptability  
At this point, model's accuracy, novel identifiability, failure isolability, explanation facility, damage index estimation, and online data processing capability have been tested. The final requirement for the model is to be reusable, adapting to data changes over time. This requirement was tested using another data subset collected a week after the subset used for anomaly detection and diagnosis, which belonged to the same die. To test these experiments, synthetic failures were generated from the new test data split by using the same procedure explained in Section 2.2. Firstly, the 2D-CNN-AE model trained in Section 3.1 was loaded and executed in the subset of data of the same die 7 days later, calculating F1 score using correct and all failure types on test data. A statistical comparison between original workorder's data and data of new workorder is presented in Figure 9, which shows differences mainly in speed and consumption standard deviation. The model does not work well with new data without being modified, obtaining an average F1 score of 0.77 on failure data. The other models developed at anomaly detection section achieved even lower F1 scores, meaning that they were unable to model new data. At this point, transfer learning experiments were executed in 2D-CNN-AE model, expecting it to adapt to new data given it belonged to similar EOC than data used for training the model. Transfer learning was applied by only training linear layers of the model while keeping original convolutional layers. The results showed that only 10 normal strokes were required for model retraining and 5 normal strokes for validation to achieve a F1 score of 1 in all failure types. Figure 10 contains images of damage indexes on test correct data before and after retraining, showing that all indexes moved below the anomaly threshold after transfer learning. This means that threshold adaptation is not required when internal layers of the autoencoder are retrained for new data of the same die. In addition, Figure 11 contains damage indexes of correct and  
Figure 9: Statistical distribution of two continuous workorders’ stroke variables with a time difference of 7 days, represented by blue and orange colors. The average sensor signal or a workorder is represented by a continuous line, and its standard deviation is represented by a semi-transparent envelope range of that signal’s color. each failure type data before and after retraining the model. It contains different groups of damage index values for each failure type, which correspond to failure magnitudes. These experiments show that transfer learning enables model reusability for data of the same die along different time periods. Retraining only linear layers of the model requires less data, training resources, and achieves better results than training the whole model from zero. The experiments were repeated with data of other workorders of the same die, achieving comparable results to the explained in this section. Finally, model adaptability to other die using transfer learning was tested, executing previous experiments with different workorders' data of another die. When executing the model without retraining, all damage indexes were far above the anomaly threshold. Afterwards, transfer learning was performed on linear layers with all training and validation strokes of this new die dataset, given that using a small number for retraining resulted in fast overfitting. After this transfer learning, damage indexes were analyzed, and they were still far beyond the anomaly threshold, which indicated that different dies require different anomaly threshold. This analysis is presented in Figure 12, which contains train, validation, test correct, and test failure damage indexes before and after transfer learning. Figure 11: Damage indexes of correct and failure test data before (**left**) and after transfer learning (**right**) using 2D-CNN-AE model in data of same die one week later. Correct damage indexes are represented in orange and failure damage indexes in red.