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(from Chapter 1: Invited Addresses and Tutorials on Signals, Coding,
 Systems and Intelligent Techniques)
| Full Citation and Abstract
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Title: |
A multi-model approach to fatigue crack growth monitoring and prediction |
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Author(s): |
V. C. Moussas, S. K. Katsikas |
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Address: |
School of Technological Applications (STEF),
Tech. Educ. Inst. of Athens, Egaleo GR-12210, Greece
Dept. of Inf. & Comm. Systems,
Univ. of the Aegean, Karlovassi 83200, Greece
vmouss @ teiath.gr, ska @ aegean.gr |
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Reference: |
SSIP-SP1, 2005 pp. 57 - 61 |
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Abstract/
Summary |
In this paper an efficient multi-model partitioning algorithm (MMPA) for parameter identification, the so-called Adaptive Lainiotis Filter (ALF), is applied to the problem of Fatigue Crack Growth (FCG) monitoring and identification in order to improve the prediction of the final crack or residual time to failure. The MMPA and Extended Kalman Filter (EKF) algorithms are both tested in order to compare their efficiency. Through extensive analysis and simulation it is demonstrated that the MMPA has superior performance both in parameter identification, as well as, in predicting the remaining lifetime to failure. Furthermore it is shown that the MMPA is fast when implemented in a parallel/distributed-processing mode and it is more robust and converges sooner than the augmented EKF. |
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