A nonlinear mixed-effects model for degradation data obtained from in-service inspections

Monitoring of degradation and predicting its progression using periodic inspection data are important to ensure safety and reliability of engineering systems. Traditional regression models are inadequate in modeling the periodic inspection data, as it ignores units specific random effects and potent...

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Bibliographic Details
Published in:Reliability engineering & system safety 2009-02, Vol.94 (2), p.509-519
Main Authors: Yuan, X.-X., Pandey, M.D.
Format: Article
Language:English
Subjects:
Applied sciences
Degradation
Exact sciences and technology
Inspection
Mixed effects
Operational research and scientific management
Operational research. Management science
Prediction
Regression model
Reliability
Reliability theory. Replacement problems
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Summary:Monitoring of degradation and predicting its progression using periodic inspection data are important to ensure safety and reliability of engineering systems. Traditional regression models are inadequate in modeling the periodic inspection data, as it ignores units specific random effects and potential correlation among repeated measurements. This paper presents an advanced nonlinear mixed-effects (NLME) model, generally adopted in bio-statistical literature, for modeling and predicting degradation in nuclear piping system. The proposed model offers considerable improvement by reducing the variance associated with degradation of a specific unit, which leads to more realistic estimates of risk.
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2008.06.013