Material Degradation Inverse Identification for Cantilever Beams Using Experimental Frequency Response Function
Qi Chen, Carol Featherston, David Kennedy, Abhishek Kundu
TL;DR
This paper introduces a new method to detect material degradation in cantilever beams using frequency response data and Bayesian inference.
Contribution
A novel stochastic framework with adaptive constraints for identifying material degradation in structures is proposed.
Findings
The KL expansion reduces dimensionality in material degradation modeling.
The two-phase constraint strategy improves physical plausibility and algorithmic stability.
Experimental validation shows accurate localization of degradation in steel beams.
Abstract
This paper presents a stochastic framework for the inverse identification of structural material degradation (SMD) in cantilever beams. The method combines the Karhunen–Loéve (KL) expansion for the efficient parameterisation of spatially varying material decay with experimental Frequency Response Function (FRF) data within a Bayesian inference scheme. This approach employs a low-dimensional spectral parameterisation via the KL expansion, which mitigates the curse of dimensionality inherent in element-wise model updating, and provides a full-field probabilistic description of SMD. A two-phase constraint strategy was developed to address the fundamental tension between physical plausibility and algorithmic stability of the inverse identification algorithm: (1) physical regularisation during identification stabilises the ill-posed inverse problem, and (2) post-convergence selective…
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Taxonomy
TopicsStructural Health Monitoring Techniques · Ultrasonics and Acoustic Wave Propagation · Machine Fault Diagnosis Techniques