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A bayesian probabilistic approach to structural health monitoring


Vanik, Michael William (1997) A bayesian probabilistic approach to structural health monitoring. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/ve6c-w550.


A Bayesian probabilistic methodology for on-line structural health monitoring which addresses the issue of parameter uncertainty inherent in problem is presented. The method uses modal parameters for a limited number of modes identified from measurements taken at a restricted number of degrees of freedom of a structure as the measured structural data. The application presented uses a linear structural model whose stiffness matrix is parameterized to develop a class of possible models. Within the Bayesian framework, a joint probability density function (PDF) for the model stiffness parameters given the measured modal data is determined. Using this PDF, the marginal PDF of the stiffness parameter for each substructure given the data can be calculated.

Monitoring the health of a structure using these marginal PDFs involves two steps. First, the marginal PDF for each model parameter given modal data from the undamaged structure is found. The structure is then periodically monitored and updated marginal PDFs are determined. A measure of the difference between the calibrated and current marginal PDFs is used as a means to characterize the health of the structure. A procedure for interpreting the measure for use by an expert system in on-line monitoring is also introduced.

The probabilistic framework is developed in order to address the model parameter uncertainty issue inherent in the health monitoring problem. To illustrate this issue, consider a very simplified deterministic structural health monitoring method. In such an approach, the model parameters which minimize an error measure between the measured and model modal values would be used as the "best" model of the structure. Changes between the model parameters identified using modal data from the undamaged structure and subsequent modal data would be used to find the existence, location and degree of damage. Due to measurement noise, limited modal information, and model error, the "best" model parameters might vary from one modal dataset to the next without any damage present in the structure. Thus, difficulties would arise in separating normal variations in the identified model parameters based on limitations of the identification method and variations due to true change in the structure. The Bayesian framework described in this work provides a means to handle this parametric uncertainty.

The probabilistic health monitoring method is applied to simulated data and laboratory data. The results of these tests are presented.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:structural health monitoring ; bayesian probability
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Applied Mechanics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Beck, James L.
Thesis Committee:
  • Beck, James L. (chair)
Defense Date:2 June 1997
Other Numbering System:
Other Numbering System NameOther Numbering System ID
Funding AgencyGrant Number
Earthquake Research Affiliates Program of CaltechUNSPECIFIED
Record Number:CaltechThesis:03142014-104500651
Persistent URL:
Related URLs:
URLURL TypeDescription DocumentTechnical Report EERL 97-07 in CaltechAUTHORS
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:8134
Deposited By: Kathy Johnson
Deposited On:18 Mar 2014 23:07
Last Modified:16 Apr 2021 23:14

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