Stochastic Modeling of Deterioration Processes through Dynamic Bayesian Networks

A generic framework for stochastic modeling of deterioration processes is proposed, based on dynamic Bayesian networks. The framework facilitates computationally efficient and robust reliability analysis and, in particular, Bayesian updating of the model with measurements, monitoring, and inspection...

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Bibliographic Details
Published inJournal of engineering mechanics Vol. 135; no. 10; pp. 1089 - 1099
Main Author Straub, Daniel
Format Journal Article
LanguageEnglish
Published Reston, VA American Society of Civil Engineers 01.10.2009
Subjects
Applied sciences
Exact sciences and technology
Fracture mechanics (crack, fatigue, damage...)
Fundamental areas of phenomenology (including applications)
Measurement and testing methods
Operational research and scientific management
Operational research. Management science
Physics
Reliability theory. Replacement problems
Solid mechanics
Structural and continuum mechanics
TECHNICAL PAPERS
Process dynamics
Bayes estimation
Markov process
Probabilistic approach
Stochastic processes
Rupture
Cracking
Inspection
Fatigue
Stochastic process
Real time
Modeling
Fatigue crack
Crack propagation
Model matching
Surveillance
Deterioration
Bayes network
System identification
Bayesian analysis
Monitoring
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Summary:A generic framework for stochastic modeling of deterioration processes is proposed, based on dynamic Bayesian networks. The framework facilitates computationally efficient and robust reliability analysis and, in particular, Bayesian updating of the model with measurements, monitoring, and inspection results. These properties make it ideally suited for near-real time applications in asset integrity management and deterioration control. The framework is demonstrated and investigated through two applications to probabilistic modeling of fatigue crack growth.
ISSN:0733-9399
1943-7889
DOI:10.1061/(ASCE)EM.1943-7889.0000024