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A Bayesian analysis for the quantification of strength model uncertainty factor of ship structures in ultimate limit state
Marine Structures
2023
92
November
103495
Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems
Reliability Engineering & System Safety
2023
239
November
109496
On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration
Data-Centric Engineering
2023
4
January
e17
Investigation of Inspection and Maintenance Optimization with Deep Reinforcement Learning in Absence of Belief States
ICASP14 - 14th International Conference on Application of Statistics and Probability in Civil Engineering
2023
State Space Kriging model for emulating nonlinear stochastic dynamical systems with parameter uncertainty
Mechanical Systems and Signal Processing
2026
243
January
113691
Estimating Rare Event Probabilities with Stein Variational Gradient Descent
International Journal for Uncertainty Quantification
2026
16
1
79--100
Branch-and-bound algorithm for efficient reliability analysis of general coherent systems
Structural Safety
2026
118
January
102653
Uncertainty Quantification for Earthquake and Tsunami Risk Analysis with Application to Valparaíso, Chile
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
2026
12
1
March
04025091
A hierarchical Bayesian framework for model-based prognostics
In prognostics and health management (PHM) of engineered systems, maintenance decisions are ideally informed by predictions of a system's remaining useful life (RUL) based on operational data. Model-based prognostics algorithms rely on a parametric model of the system degradation process. The model parameters are learned from real-time operational data collected on the system. However, there can be valuable information in data from similar systems or components, which is not typically utilized in PHM. In this contribution, we propose a hierarchical Bayesian modeling (HBM) framework for PHM that integrates both operational data and run-to-failure data from similar systems or components. The HBM framework utilizes hyperparameter distributions learned from data of similar systems or components as priors. It enables efficient updates of predictions as more information becomes available, allowing for increasingly accurate assessments of the degradation process and its associated variability. The effectiveness of the proposed framework is demonstrated through two experimental applications involving real-world data from crack growth and lithium battery degradation. Results show significant improvements in RUL prediction accuracy and demonstrate how the framework facilitates uncertainty management through predictive distributions.
2026
Hierarchical Bayesian Modeling for Uncertainty Quantification in Simplified Tunnel Deformation Models
Proc. of the 9th International Symposium on Geotechnical Safety and Risk (ISGSR)
Research Publishing, Singapore
2025