scholarly journals Bayesian dynamic linear models for structural health monitoring

2017 ◽  
Vol 24 (12) ◽  
Author(s):  
James‐A. Goulet
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Linear Models ◽  
Dynamic Linear Models ◽  
Structural Health

scholarly journals Structural Health Monitoring under Nonlinear Environmental or Operational Influences

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jyrki Kullaa
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Linear Models ◽  
Nonlinear Models ◽  
Novelty Detection ◽  
Minimum Mean Square Error ◽  
Simulated Data ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Structural Health

Vibration-based structural health monitoring is based on detecting changes in the dynamic characteristics of the structure. It is well known that environmental or operational variations can also have an influence on the vibration properties. If these effects are not taken into account, they can result in false indications of damage. If the environmental or operational variations cause nonlinear effects, they can be compensated using a Gaussian mixture model (GMM) without the measurement of the underlying variables. The number of Gaussian components can also be estimated. For the local linear components, minimum mean square error (MMSE) estimation is applied to eliminate the environmental or operational influences. Damage is detected from the residuals after applying principal component analysis (PCA). Control charts are used for novelty detection. The proposed approach is validated using simulated data and the identified lowest natural frequencies of the Z24 Bridge under temperature variation. Nonlinear models are most effective if the data dimensionality is low. On the other hand, linear models often outperform nonlinear models for high-dimensional data.

scholarly journals Incorporation of Structural Health Monitoring Coupled Data in Dynamic Extreme Stress Prediction of Steel Bridges Using Dynamic Coupled Linear Models

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xueping Fan ◽  
Zhipeng Shang ◽  
Guanghong Yang ◽  
Xiaoxiong Zhao ◽  
Yuefei Liu
Keyword(s):  
Time Series ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Linear Models ◽  
Steel Bridges ◽  
Steel Bridge ◽  
Structural Health ◽  
Extreme Stress ◽  
Stress Prediction ◽  
Bridge Reliability

In this article, an approach for using structural health monitoring coupled extreme stress data in dynamic extreme stress prediction of steel bridges is presented, where the coupled extreme stress data means the extreme stress data with dynamicity, randomness, and trend. Firstly, the modeling processes about dynamic coupled linear models (DCLM) are provided based on a supposed coupled time series; furthermore, the dynamic probabilistic recursion processes about DCLM are given with Bayes method; secondly, the monitoring dynamic coupled extreme stress data is taken as a time series, historical monitoring coupled extreme stress data-based DCLM and the corresponding Bayesian probabilistic recursion processes are given for predicting bridge extreme stresses; furthermore, the monitoring mechanism is provided for monitoring the prediction precision of DCLM; finally, the monitoring coupled extreme stress data of a steel bridge is used to illustrate the proposed approach which can provide the foundations for bridge reliability prediction and assessment.

Structural Health Monitoring With Autoregressive Support Vector Machines

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
Luke Bornn ◽  
Charles R. Farrar ◽  
Gyuhae Park ◽  
Kevin Farinholt
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Linear Models ◽  
Vibration Sensor ◽  
Structural Vibration ◽  
Support Vector ◽  
Detection Techniques ◽  
Structural Health

The use of statistical methods for anomaly detection has become of interest to researchers in many subject areas. Structural health monitoring in particular has benefited from the versatility of statistical damage-detection techniques. We propose modeling structural vibration sensor output data using nonlinear time-series models. We demonstrate the improved performance of these models over currently used linear models. Whereas existing methods typically use a single sensor’s output for damage detection, we create a combined sensor analysis to maximize the efficiency of damage detection. From this combined analysis we may also identify the individual sensors that are most influenced by structural damage.

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