Afrânio Melo

### How we are today BibMon is based on multivariate statistical process monitoring and machine learning, aiming to establish robust monitoring procedures by identifying relevant relationships among several variables. ###...

### 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...

### How we are today To improve interaction and adoption by users and operators at process industries, it is important that models are interpretable. At present, the interpretability functionalities provided...

### How we are today Reconstruction models aim to represent normal process behavior by utilizing a mathematical structure that incorporates all relevant variables simultaneously. The objective is typically to create...

### How we are today BibMon currently supports the following regression models: - Echo State Networks (ESN); - all regression models that use the [scikit-learn](https://scikit-learn.org/) interface. The second item significantly...

### How we are today Data preprocessing is handled by the `PreProcess` class, which contains various preprocessing methods such as normalization, removal of NaN observations, and more. These self-contained methods...

### How we are today BibMon monitors a specific metric called Squared Prediction Error (SPE). Various alarm logics can be used to detect specific variations in this metric. Traditionally, an...

### How we are today Currently, BibMon includes two datasets packaged with it: the Tennessee Eastman Process dataset and an anonymized dataset containing real process data. These datasets can be...