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Statistical Methodology for Optimal Sensor Locations for Damage Detection in Structures (7 pages)
By J.L. Beck, E. Chan and C. Papadimitriou, California Institute of Technology  

Conference: 1998 IMAC XVI - 16th International Modal Analysis Conference
A Bayesian statistical methodol-ogy is presented for optimally locating the sensors in a structure for the purpose of extracting the most information about the model parameters which can be used in model updating and in damage detection and localization. This statistical approach properly handles the unavoidable uncertainties in the mea-sured data as well as the uncertainties in the math-ematical model used to represent the structural be-havior. The optimality criterion for the sensor lo-cations is based on information entropy which is a measure of the uncertainty in the model parame-ters. The uncertainty in these parameters is com-puted by the Bayesian statistical methodology and then the entropy measure is minimized over the set of possible sensor configurations using a genetic al-gorithm. Results presented illustrate how both the minimum entropy of the parameters and the opti-mal sensor configuration depend on the location of sensors, number of sensors, number and type of con-tributing modes and the structural parameterization (substructuring) scheme used.
 

 

 

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