A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis

A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis

This work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedu...

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Bibliographic Details
Main Authors:	James E. Warner, Geoffrey F. Bomarito, Jacob D. Hochhalter, William P. Leser, Patrick E. Leser, John A. Newman
Format:	Article
Language:	English
Published:	The Prognostics and Health Management Society 2017-06-01
Series:	International Journal of Prognostics and Health Management
Subjects:	uncertainty quantification surrogate modeling damage diagnosis
Online Access:	https://papers.phmsociety.org/index.php/ijphm/article/view/2637

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