lucadellalib/bdl-rul-svgd
Bayesian deep learning for remaining useful life estimation via Stein variational gradient descent
This project helps engineers and maintenance professionals predict how much longer critical machinery, like aircraft engines, will operate before needing repair or replacement. It takes historical operational data and sensor readings as input, and outputs an estimated 'remaining useful life' with a confidence range. This tool is designed for reliability engineers, asset managers, or data scientists working in predictive maintenance.
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Use this if you need to precisely forecast the lifespan of mechanical components and want to understand the uncertainty in those predictions.
Not ideal if you are looking for a plug-and-play solution without expertise in deep learning model training and evaluation.
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Language
Python
License
Apache-2.0
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Last pushed
Feb 05, 2024
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