Add support for classification-based models

[afraniomelo]

How we are today

Currently, BibMon uses a deviation-based methodology, which focuses on monitoring deviations from expected values or patterns. In this framework, algorithms are designed to compare actual sensor measurements with model representations to identify process anomalies. A metric called SPE (Squared Prediction Error) is used to compute the deviation between actual and expected values. SPE values are computed from regression or reconstruction models. For more details, please check https://doi.org/10.1016/j.dche.2024.100182.

Proposed new feature

Some applications would benefit more from classification models rather than regression or reconstruction models. In this scenario, the model would analyze sample data and attribute a probability indicating the likelihood that the data corresponds to a faulty event. This probability would be analogous to the SPE currently used in the package.

Implementation

Implementing this new feature may significantly impact the package structure, as the main class, GenericModel, is entirely based on the use of SPE to create control charts. One approach could be to create a new class called GenericModelProb, which would be analogous to the existing GenericModel. This new class could generate control charts based on probabilities instead of SPE values.