AI RESEARCH
Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health
arXiv CS.LG
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ArXi:2604.20735v1 Announce Type: new Accurate condition monitoring of industrial equipment requires inferring latent degradation parameters from indirect sensor measurements under uncertainty. While traditional Bayesian methods like Marko Chain Monte Carlo (MCMC) provide rigorous uncertainty quantification, their heavy computational bottlenecks render them impractical for real-time process control.