Adaptive Estimation of FCG Using Nonlinear State-Space Models

In this paper, an efficient adaptive nonlinear algorithm for estimation and identification, the so-called adaptive Lainiotis filter (ALF), is applied to the problem of fatigue crack growth (FCG) estimation, identification, and prediction of the final crack (failure). A suitable nonlinear state-space...

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Bibliographic Details
Published in:Stochastic analysis and applications 2005-07, Vol.23 (4), p.705-722
Main Authors: Moussas, Vassilios C., Katsikas, Sokratis K., Lainiotis, Demetrios G.
Format: Article
Language:English
Subjects:
Adaptive algorithms
ALF
Applications
EKF
Exact sciences and technology
Failure prediction
Fatigue crack growth
Inference from stochastic processes
time series analysis
Mathematics
Nonlinear FCG models
Nonlinear prediction
Primary 93E10
Probability and statistics
Probability theory and stochastic processes
Reliability, life testing, quality control
Sciences and techniques of general use
Secondary 60G35
Statistics
Stochastic analysis
Stochastic models
Stochastic processes
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Summary:In this paper, an efficient adaptive nonlinear algorithm for estimation and identification, the so-called adaptive Lainiotis filter (ALF), is applied to the problem of fatigue crack growth (FCG) estimation, identification, and prediction of the final crack (failure). A suitable nonlinear state-space FCG model is introduced for both ALF and extended Kalman filter (EKF). Both algorithms are tested in order to compare their efficiency. Through extensive analysis and simulation, it is demonstrated that the ALF has superior performance both in FCG estimation, as well as in predicting the remaining lifetime to failure. Furthermore, it is shown that the ALF is faster and easier to implement in a parallel/distributed processing mode, and much more robust than the classic EKF.
ISSN:0736-2994
1532-9356
DOI:10.1081/SAP-200064462