## 2 Materials and Methods
### Experimenting Procedure
The forming processes applied on input working stations are usually related to bending, which require higher forces than folding and cutting that are performed in more advanced working stations. The configuration of each working station is independent, and therefore the forces that the press machine applies are the result of adding all the forces applied along its working stations. A wrong configuration in the work stations near the machine's output can increase the load applied in this part of the machine significantly, thus causing the misalignment of forces by performing higher loads in the output side than in the input side. The machine is not designed to work correctly in this situation and therefore, its components can be damaged. Another possible application of this synthetic failure can be detecting cracks and misalignments in the input connecting rods. When the applied forces are higher in the output working stations than in the input ones, the input connecting rods may be unable to support the applied forces. \begin{table}
\begin{tabular}{l l} \hline \hline
**Synthetic Failure** & **Signal Modification and Variations** \\ \hline Force increment, simulating harder input material & Increase forces on full signal 4 axis (+10\%, +25\%, +50\% and +100\%) \\ Die misadjustment, simulating press force anticipation & Anticipation of press force application (only 50\({}^{\circ}\) and 100\({}^{\circ}\)) \\ Machine degradation, higher power consumption for same forces & Increase power consumption on full signal (+10\%, +25\%, +50\% and +100\%) \\ Unbalanced loads & Exchange press input and press output forces on full signal \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Synthetic failures and corresponding signal modifications used for model validation. Two original stroke signals and four failure signals, one failure for each failure class, are presented in Figure 2. In addition, different magnitudes of these failure types have the same shape but vary according to specifications of Table 1.  
For data preparation, data were first grouped by die ID and workorders, and used these data subsets for experimenting. Each workorder contains process data collected in a specific continuous time interval no longer than a week, which is delimited by production working periods. This grouping system is suitable for press machines given that each die works under similar operating conditions and performs the same forming process. Then, data were analyzed divided for model creation and validation based on industrial production data order: first 80% strokes were used for training and validation, which was further divided into 90% for training and 10% for validation, whereas last 20% was used for testing. The resulting number of stroke samples used for training, validation and testing in anomaly detection are 516, 58, and 143, respectively. Similarly, the number of stroke samples used for training, validation and testing for adaptability are 495, 55, and 137, as summarized in Table 2. The training data split contains strokes collected in a time period of 12 h, but this number can vary regarding the number of strokes and time period of each workorder. Afterwards, data were cleaned and preprocessed to prepare it for the PdM models, and then compared their results. For data cleaning, domain technicians defined filters using plots, correlations, and univariate statistical measures such as mean, in order to discard any anomalous stroke resulted from acquisition problems or missing data. Models were trained under the assumption that 90% of training data was correct and the remaining could have outliers. This assumption was concluded after domain technicians analyzed training strokes using domain-based ratios and expertise to quantify the probability of outliers in the data. Next, a model evaluation and ranking strategy was defined, which uses the F1 metric for overall performance analysis, and precision and recall for further analysis.