Robust Optimal Sensor Placement for Uncertain Structures with Interval Parameters

This paper proposes a robust optimal sensor placement method for structural health monitoring considering uncertainty. To avoid the deficiencies associated with scarce statistical information, a non-probabilistic approach is applied to cope with uncertainties in the optimal sensor placement field. Based on an interval analysis approach and a modal analysis method, an interval Fisher information matrix (IFIM) is derived from the deterministic case, and the bounds of the IFIM eigenvalues are obtained. To realize the optimization process, the determinant of the IFIM, composed of the interval central and radius values corresponding to performance and fluctuation, is regarded as an optimization function. Following normalization and using weighted coefficients, the robust optimal sensor placement method with uncertain intervals can be transformed into a deterministic optimization process. Therefore, a single-objective optimization process can replace the two-objective optimization including the central and radius values. Because of the global optimization ability of modern intelligent algorithms, a genetic algorithm is adopted to determine the best sensor placement layout, with the node location used as the design variable. The validity of the proposed method is proved using three numerical examples.