A methodology for sensor number and placement optimization for vibration-based damage detection of composite structures under model uncertainty

Structural health monitoring techniques for composite structures are dependent on the data acquired from sensors; thus, optimal sensor network design is important to provide adequate and reliable information. This paper presents a novel framework for optimizing the number of sensors and sensor placement under model uncertainty for vibration-based damage detection in composite structures. The number of target vibration modes to be identified is first determined, based on the sum of modal effective mass fractions, to sufficiently capture dynamic responses. Since any point on the structure can be a candidate sensor position (causing a large design space), a modal kinetic-energy-based index is proposed to narrow the design space. Design objectives simultaneously minimize the number of sensors and the mean and standard deviation values for the root mean square error of off-diagonal terms in the modal assurance criterion matrix. The nondominated sorting genetic algorithm II is adopted to solve this problem. Monte Carlo simulation (MCS) is applied to evaluate the latter two objective functions. To reduce computation costs, real performance evaluations in MCS are replaced with Gaussian process regression models. To validate the optimized sensors, an optimization-based delamination detection process is applied. Case studies are presented to demonstrate the developed optimization framework.

Automated summary

This paper introduces a novel framework for optimizing the number and placement of sensors in vibration-based damage detection in composite structures. The framework incorporates modal effective mass fractions to determine target vibration modes and proposes a modal kinetic-energy-based index to reduce the design space. The optimization problem is solved using the nondominated sorting genetic algorithm II and Monte Carlo simulation.