Optimal sensor placement for enhancing sensitivity to change in stiffness for structural health monitoring

Abstract

This paper focuses on optimal sensor placement for structural health monitoring (SHM), in which the goal is to find an optimal configuration of sensors that will best predict structural damage. The problem is formulated as a bound constrained mixed variable programming (MVP) problem, in which the discrete variables are categorical; i.e., they may only take on values from a pre-defined list. The problem is particularly challenging because the objective function is computationally expensive to evaluate and first-order derivatives may not be available. The problem is solved numerically using the generalized mixed variable pattern search (MVPS) algorithm. Some new theoretical convergence results are proved, and numerical results are presented, which show the potential of our approach.

Constrained optimization of an idealized Y-shaped baffle for the Fontan surgery at rest and exercise

“…The SMF method [5] together with MADS has been successfully applied to the acoustic optimization of an airfoil trailingedge [8][9][10], the optimization of sensor placement for structural health monitoring [41], and the optimization of surface structure of nanomaterials [42].…”

Modeling without categorical variables: a mixed-integer nonlinear program for the optimization of thermal insulation systems

“…We illustrate our approach on an example of thermal insulation systems and emphasize the interaction of integer and nonlinear modeling techniques. Our approach provides a blueprint for reformulating other design problems that involve categorical variables, for example the design of nanomaterials (Zhao et al 2006), and in optimal sensor placement (Beal et al 2008). We believe that the conclusions of this paper also are relevant to application scientists who develop simulation tools.…”

Data correlation analysis for optimal sensor placement using a bond energy algorithm

“…Lu et al [10] proposed a method for optimal sensor placement using a distance measure matrix and synthesized support degree, in which the number and placement of accelerometers were determined by the values of the synthesized support degree. The aforementioned optimal sensor placement methods are mostly used for modal identification, the optimal sensor placement methods were also additional studied with the aim to the structural response reconstruction and monitoring [11], such like the optimal placement of sensors for sub-surface fatigue crack monitoring [12], the sensor positioning and choice of the number of sensors were optimized in terms of the reconstruction on the temperature field considering the error propagation in case of uncertain measurements [13], the optimal sensor placement for enhancing sensitivity to change in stiffness was proposed to find the optimal configuration of sensors that would best predict structural damage [14], the optimal sensor placement methodology was proposed so as to better estimate the vibration response of the entire structure [15] and so on. The stress distribution and displacement development of the structure are important monitoring parameters for structural safety estimation and should be considered in the optimal sensor placement method for such strain or displacement sensors.…”