petrobras/BibMon
Python package that provides predictive models for fault detection, soft sensing, and process condition monitoring.
This package helps process engineers monitor industrial systems by using historical process data to build predictive models. You input data from your operational processes, and it outputs real-time alerts when deviations indicate potential faults or anomalies. Process control engineers, plant operators, and anyone responsible for maintaining industrial equipment can use this to detect problems early.
No commits in the last 6 months. Available on PyPI.
Use this if you need to detect subtle deviations in your process data that could signal equipment malfunctions, faults, or abnormal operating conditions.
Not ideal if you're looking for a general-purpose anomaly detection tool for non-industrial data or if you don't have historical data representing normal process conditions.
Stars
99
Forks
50
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 11, 2025
Commits (30d)
0
Dependencies
8
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/petrobras/BibMon"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
hustcxl/Deep-learning-in-PHM
Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction
kokikwbt/predictive-maintenance
Datasets for Predictive Maintenance
biswajitsahoo1111/rul_codes_open
This repository contains code that implement common machine learning algorithms for remaining...
tvhahn/weibull-knowledge-informed-ml
Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets....
awslabs/aws-fleet-predictive-maintenance
Predictive Maintenance for Vehicle Fleets