A parametric investigation of state-space-based prediction error methods with stochastic excitation for structural health monitoring

2007 ◽  
Vol 16 (5) ◽  
pp. 1621-1638 ◽  
Author(s):  
L A Overbey ◽  
C C Olson ◽  
M D Todd
Keyword(s):  
Structural Health Monitoring ◽  
State Space ◽  
Health Monitoring ◽  
Prediction Error ◽  
Stochastic Excitation ◽  
Structural Health ◽  
Parametric Investigation ◽  
Prediction Error Methods

An Evaluation of Damage Features Extraction from Attractors in Structural Health Monitoring

2011 ◽  
Vol 105-107 ◽  
pp. 738-741
Author(s):  
Chao Xu ◽  
Dong Wang
Keyword(s):  
Structural Health Monitoring ◽  
State Space ◽  
Health Monitoring ◽  
Prediction Error ◽  
Structural Response ◽  
Accurate Information ◽  
Variance Ratio ◽  
Damage Level ◽  
Structural Health ◽  
Local Variance

Structural health monitoring provides accurate information about structure’s safety and integrity. The vibration-based structural health monitoring involves extracting a feature which robustly quantifies damage induced change to the structure. Recent work has focused on damage features extracted from the state space attractor of the structural response. Some of these features involve prediction error and local variance ratio. In the present paper, a five degree of freedom spring damper system forced by a Lorenz excitation is used to evaluate these two typical damage features. Their ability of identification damage level and location is characterized and compared.

scholarly journals Robust structural health monitoring under environmental and operational uncertainty with switching state-space autoregressive models

2018 ◽  
Vol 18 (2) ◽  
pp. 435-453 ◽  
Author(s):  
Anthony Liu ◽  
Lazhi Wang ◽  
Luke Bornn ◽  
Charles Farrar
Keyword(s):  
Structural Health Monitoring ◽  
State Space ◽  
Health Monitoring ◽  
Structural Response ◽  
Probability Models ◽  
Time Step ◽  
Operational Conditions ◽  
Structural Health ◽  
Novel Approach ◽  
Definition Of

Existing methods for structural health monitoring are limited due to their sensitivity to changes in environmental and operational conditions, which can obscure the indications of damage by introducing nonlinearities and other types of noise into the structural response. In this article, we introduce a novel approach using state-space probability models to infer the conditions underlying each time step, allowing the definition of a damage metric robust to environmental and operational variation. We define algorithms for training and prediction, describe how the algorithm can be applied in both the presence and absence of measurements for external conditions, and demonstrate the method’s performance on data acquired from a laboratory structure that simulates the effects of damage and environmental and operational variation on bridges.

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