Sensor Placement Optimization Under Uncertainty for Structural Health Monitoring Systems of Hot Aerospace Structures.

Abstract

A methodology for the optimum design under uncertainty of sensor arrays for structural health monitoring systems is developed. Stochastic finite element analysis, damage detection algorithms, and nonlinear optimization are integrated for sensor placement optimization under uncertainty. The stochastic finite element analysis incorporates uncertainties and spatial variability in dynamic mechanical loads, material properties, and structural geometry through random process/field techniques. Damage detection algorithms consist of feature extraction, feature selection, and state classification and aid in the prediction of sensor layout performance via probabilistic performance measures. The basic probabilistic finite element models as well as the sensor layout performance prediction method are assessed for validation prior to their utilization in sensor placement optimization. Several validation metrics are investigated for comparison of predicted natural frequencies, mode shapes, and probabilistic performance measures to corresponding experimental observations. The structural health monitoring sensors are required to be placed optimally in order to detect with high probability and reliability any structural damage before it becomes critical. A global-local approach that combines quadratic local approximations of the objective function with a branch and fit technique is used to optimize several probabilistic performance measures and multi-objective performance functions. The proposed methodology is illustrated for application on a prototype component of a thermal protection system.