BibMon: An open source Python package for process monitoring, soft sensing, and fault diagnosis

• Published in: Digital Chemical Engineering Vol. 13; p. 100182
• Main Authors: Melo, Afrânio, Lemos, Tiago S.M., Soares, Rafael M., Spina, Deris, Clavijo, Nayher, Campos, Luiz Felipe de O., Câmara, Maurício Melo, Feital, Thiago, Anzai, Thiago K., Thompson, Pedro H., Diehl, Fábio C., Pinto, José Carlos
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2024; Elsevier
• Subjects: Fault detection and diagnosis; Machine learning; Open source; Process monitoring; Soft sensor; Process monitoring; Soft sensor; Open source; Fault detection and diagnosis; Machine learning
• ISSN: 2772-5081; 2772-5081
• DOI: 10.1016/j.dche.2024.100182

This paper introduces BibMon, a Python package that provides predictive models for data-driven fault detection and diagnosis, soft sensing, and process condition monitoring. Key features include regression and reconstruction models, preprocessing pipelines, alarms, and visualization through control charts and diagnostic maps. BibMon also includes real and simulated datasets for benchmarking, comparative performance analysis of different models, and hyperparameter tuning. The package is designed to be highly extensible, allowing for easy integration of new models and methodologies through its object-oriented implementation. Currently, BibMon is in production at Petrobras, a major player in the energy industry, monitoring numerous industrial assets and enabling real-time detection and diagnosis of equipment and process faults. The software is open source and available at: https://github.com/petrobras/bibmon.