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Autoregressive modeling with state-space embedding vectors for damage detection under operational and environmental variability
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TLDR
A hypothesis test is established that the MAR model will fail to predict future response if damage is present in the test condition, and this test is investigated for robustness in the context of operational and environmental variability.Abstract:
A nonlinear time series approach is presented to detect damage in systems by using a state-space reconstruction to infer the geometrical structure of a deterministic dynamical system from observed time series response at multiple locations. The unique contribution of this approach is using a Multivariate Autoregressive (MAR) model of a baseline condition to predict the state space, where the model encodes the embedding vectors rather than scalar time series. A hypothesis test is established that the MAR model will fail to predict future response if damage is present in the test condition, and this test is investigated for robustness in the context of operational and environmental variability. The applicability of this approach is demonstrated using acceleration time series from a base-excited 3-story frame structure.read more
Citations
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Journal ArticleDOI
A Bayesian approach based on a Markov-chain Monte Carlo method for damage detection under unknown sources of variability
TL;DR: An algorithm with a Bayesian approach based on a Markov-chain Monte Carlo method to cluster structural responses of the bridges into a reduced number of global state conditions, by taking into account eventual multimodality and heterogeneity of the data distribution is proposed.
Book ChapterDOI
State Estimation in Nonlinear Structural Systems
Kalil Erazo,Eric M. Hernandez +1 more
TL;DR: Overall the proposed framework relies on the filters to perform estimation of the state (displacement and velocity) throughout the structure using a model and noise contaminated measured response at limited locations.
Book ChapterDOI
Damage Detection for Structural Health Monitoring of Bridges as a Knowledge Discovery in Databases Process
TL;DR: This chapter intends to pose damage detection in bridges in the context of the KDD process, where data transformation and data mining play major roles.
Application of modal filters for damage detection in the presence of non-linearities
TL;DR: The aim of the paper is to study the possibility of implementing modal filtering techniques for damage detection in the presence of non-linearities in the recorded signals, considering either the auto-regressive parameters or the time-domain residuals.
References
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Structural health monitoring algorithm comparisons using standard data sets
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