A recursive Bayesian framework for structural health management using online monitoring and periodic inspections

The necessary information for developing a structural health diagnostic and prognostic solution is often obtained from various sources. This paper presents a Bayesian framework for online integration of the structural health assessment information obtained from empirical crack growth models, structu...

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Bibliographic Details
Published in:Reliability engineering & system safety 2013-04, Vol.112, p.154-164
Main Authors: Rabiei, Masoud, Modarres, Mohammad
Format: Article
Language:English
Subjects:
Acoustic emission
Acoustical measurements and instrumentation
Acoustics
Bayesian analysis
Bayesian knowledge fusion
Crack growth monitoring
Crack propagation
Cracks
Damage
Empirical analysis
Exact sciences and technology
Fracture mechanics (crack, fatigue, damage...)
Fundamental areas of phenomenology (including applications)
Health
Mathematical models
Mathematics
Measurement and testing methods
Monitoring
Parametric inference
Physics
Probability and statistics
Recursive Bayesian estimation
Remaining life prediction
Sciences and techniques of general use
Solid mechanics
Statistics
Structural and continuum mechanics
Structural health management
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Summary:The necessary information for developing a structural health diagnostic and prognostic solution is often obtained from various sources. This paper presents a Bayesian framework for online integration of the structural health assessment information obtained from empirical crack growth models, structural health monitoring and periodic inspections. The data used in Bayesian updating could be direct damage observations (e.g., observed crack sizes) and/or damage growth rate estimates (e.g., crack growth rate observations). An AE-based monitoring approach is used to obtain the crack growth rate observations in this paper. The outcome of this approach is updated crack size distribution as well as updated parameters for an empirical crack growth model. The model with updated parameters is used for prognosis given an assumed future usage profile.
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2012.11.020