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A probabilistic inference framework for RUL prediction: Modeling, propagation, and quantification of uncertainty | Synapse
March 3, 2026
A probabilistic inference framework for RUL prediction: Modeling, propagation, and quantification of uncertainty
WC
Wei Chen
Fujian Medical University
WW
Weimin Wu
XL
Xing Li
Institute of Intelligent Machines
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Key Points
The framework leverages probabilistic inference to enhance remaining useful life predictions, improving decision-making processes.
Key metrics include uncertainty quantification that allows for better reliability assessment across various scenarios.
Assessment using a probabilistic model incorporates advanced techniques for modeling and propagating uncertainty in predictions.
This approach may enable significant improvements in predictive maintenance strategies in industrial applications.
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Chen et al. (Tue,) studied this question.
synapsesocial.com/papers/69a76186c6e9836116a2f890
https://doi.org/https://doi.org/10.1016/j.measurement.2026.120851
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