https://github.com/petrobras/bibmon

Python package that provides predictive models for fault detection, soft sensing, and process condition monitoring.
https://github.com/petrobras/bibmon

echo-state-network fault-detection fault-diagnosis machine-learning neural-networks pca process-monitoring process-systems-engineering scikit-learn soft-sensor time-series

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Python package that provides predictive models for fault detection, soft sensing, and process condition monitoring.

• Host: GitHub
• URL: https://github.com/petrobras/bibmon
• Owner: petrobras
• License: apache-2.0
• Created: 2024-05-23T14:51:57.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-01-13T23:10:41.000Z (10 days ago)
• Last Synced: 2025-01-15T17:55:46.989Z (8 days ago)
• Topics: echo-state-network, fault-detection, fault-diagnosis, machine-learning, neural-networks, pca, process-monitoring, process-systems-engineering, scikit-learn, soft-sensor, time-series
• Language: Python
• Homepage: https://bibmon.readthedocs.io/
• Size: 15.5 MB
• Stars: 79
• Watchers: 5
• Forks: 40
• Open Issues: 22
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION.bib

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README

        
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# BibMon

`BibMon` (from the Portuguese ***Bib**lioteca de **Mon**itoramento de Processos*, or Process Monitoring Library) is a Python package that provides deviation-based predictive models for fault detection, soft sensing, and process condition monitoring.

For further information, please refer to the [documentation](https://bibmon.readthedocs.io/) or to the [scientific publication](https://www.sciencedirect.com/science/article/pii/S2772508124000449) detailing `BibMon`.

Installation

----------------------

`BibMon` can be installed using [pip](https://pypi.org/project/bibmon/):

    pip install bibmon

Or [conda](https://anaconda.org/conda-forge/bibmon):

    conda install conda-forge::bibmon

Available Models

----------------------

* PCA (Principal Component Analysis);

* ESN (Echo State Network);

* SBM (Similarity-Based Method);

* Autoencoders;

* any regressor that uses the `scikit-learn` interface.

Usage

----------------------

Essentially, the library is used in two steps:

1. In the training step, a model is generated that captures the relationships between variables in the normal process condition;

2. In the prediction step, process data is compared to the model's predictions, resulting in deviations; if these deviations exceed a predefined limit, alarms are triggered.

Specifically, the implemented control charts are based on squared prediction error (SPE).

For more details, please refer to the [tutorials](https://bibmon.readthedocs.io/en/latest/tutorials.html) available in the documentation to learn about the main functionalities of `BibMon`. You can find the corresponding Jupyter Notebooks for these tutorials in the `docs/source/` directory.

Features

----------------------

The resources offered by `BibMon` are:

* Application in online systems: a trained `BibMon` model can be used for online analysis with both individual samples and data windows. For each sample or window, a prediction is made, the model state is updated, and alarms are calculated.

* Compatibility, within the same architecture, of regression models (i.e., virtual sensors, containing separate X and Y data, such as RandomForest) and reconstruction models (containing only X data, such as PCA).

* Preprocessing pipelines that take into account the differences between X and Y data and between training and testing stages.

* Possibility of programming different alarm logics.

* Easy extensibility through inheritance (there is a class called `GenericModel` that implements all the common functionality for various models and can be used as a base for implementing new models). For details, consult the `CONTRIBUTING.md` file.

* Convenience functions for performing automatic offline analysis and plotting control charts.

* Real and simulated process datasets available for importing.

* Comparative tables to automate the performance analysis of different models.

* Automatic hyperparameter tuning.

Contributing

----------------------

BibMon is an open-source project driven by the community. If you would like to contribute to the project, please refer to the [CONTRIBUTING.md](https://github.com/petrobras/bibmon/blob/main/CONTRIBUTING.md) file.

The package originated from research projects conducted in collaboration between the Chemical Engineering Program at COPPE/UFRJ and the Leopoldo Américo Miguez de Mello Research Center (CENPES/Petrobras).