{"id":15064000,"url":"https://github.com/petrobras/bibmon","last_synced_at":"2025-04-13T02:13:26.836Z","repository":{"id":241283965,"uuid":"804933004","full_name":"petrobras/BibMon","owner":"petrobras","description":"Python package that provides predictive models for fault detection, soft sensing, and process condition monitoring.","archived":false,"fork":false,"pushed_at":"2025-01-13T23:10:41.000Z","size":16208,"stargazers_count":83,"open_issues_count":21,"forks_count":45,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-04-13T02:13:23.028Z","etag":null,"topics":["echo-state-network","fault-detection","fault-diagnosis","machine-learning","neural-networks","pca","process-monitoring","process-systems-engineering","scikit-learn","soft-sensor","time-series"],"latest_commit_sha":null,"homepage":"https://bibmon.readthedocs.io/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/petrobras.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE.md","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.bib","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-05-23T14:51:57.000Z","updated_at":"2025-03-27T18:20:08.000Z","dependencies_parsed_at":"2024-05-28T20:45:25.987Z","dependency_job_id":"e3c8194f-c2fd-44dd-8627-a7c7591019c9","html_url":"https://github.com/petrobras/BibMon","commit_stats":{"total_commits":51,"total_committers":5,"mean_commits":10.2,"dds":"0.17647058823529416","last_synced_commit":"e5f47f356d6c361df6b65df9760aee5fc301b372"},"previous_names":["petrobras/bibmon"],"tags_count":10,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/petrobras%2FBibMon","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/petrobras%2FBibMon/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/petrobras%2FBibMon/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/petrobras%2FBibMon/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/petrobras","download_url":"https://codeload.github.com/petrobras/BibMon/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248654095,"owners_count":21140236,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["echo-state-network","fault-detection","fault-diagnosis","machine-learning","neural-networks","pca","process-monitoring","process-systems-engineering","scikit-learn","soft-sensor","time-series"],"created_at":"2024-09-25T00:09:59.731Z","updated_at":"2025-04-13T02:13:26.817Z","avatar_url":"https://github.com/petrobras.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/petrobras/BibMon/HEAD)\n[![Apache 2.0][apache-shield]][apache] \n[![CC BY 4.0][cc-by-shield]][cc-by]\n\n[apache]: https://opensource.org/licenses/Apache-2.0\n[apache-shield]: https://img.shields.io/badge/License-Apache_2.0-blue.svg\n[cc-by]: http://creativecommons.org/licenses/by/4.0/\n[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg\n\n# BibMon\n\n`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.\n\nFor 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`.\n\nInstallation\n----------------------\n\n`BibMon` can be installed using [pip](https://pypi.org/project/bibmon/):\n\n    pip install bibmon\n\nOr [conda](https://anaconda.org/conda-forge/bibmon):\n\n    conda install conda-forge::bibmon\n\nAvailable Models\n----------------------\n\n* PCA (Principal Component Analysis);\n* ESN (Echo State Network);\n* SBM (Similarity-Based Method);\n* Autoencoders;\n* any regressor that uses the `scikit-learn` interface.\n\nUsage\n----------------------\n\nEssentially, the library is used in two steps:\n\n1. In the training step, a model is generated that captures the relationships between variables in the normal process condition;\n2. 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.\n\nSpecifically, the implemented control charts are based on squared prediction error (SPE).\n\nFor 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.\n\n\nFeatures\n----------------------\n\nThe resources offered by `BibMon` are:\n\n* 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.\n* 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).\n* Preprocessing pipelines that take into account the differences between X and Y data and between training and testing stages.\n* Possibility of programming different alarm logics.\n* 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.\n* Convenience functions for performing automatic offline analysis and plotting control charts.\n* Real and simulated process datasets available for importing.\n* Comparative tables to automate the performance analysis of different models.\n* Automatic hyperparameter tuning.\n\nContributing\n----------------------\n\nBibMon 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.\n\nThe 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).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpetrobras%2Fbibmon","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpetrobras%2Fbibmon","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpetrobras%2Fbibmon/lists"}