UC San Diego

The performance gains achieved by increasingly wide adoption of composite materials in the aerospace sector remain challenged by failure modes that are not always well understood. Implementing a structural health monitoring (SHM) system enables real-time monitoring of a structure, allowing for continuous assessment of its current condition.

This work proposes a comprehensive optimal fiber optic sensor placement framework for structural health monitoring applications. The framework is applied to an aircraft’s wing spar entirely made of composite materials. The damage of interest is debonding between laminates, which may cause local buckling that leads to reduced structural load-carrying capabilities. The development and validation of a high-fidelity finite element (FE) model that is used as a synthetic data generator are presented in detail. The inputs to the model are loads and debonding damage parameters (size and location), and the outputs are uniaxial strain measurements and buckling eigenvalues. Then, this work describes “run time” surrogate models created using different machine learning methods to overcome the high computational costs of each run of the physics-based model.

Bayesian inference is used to estimate the damage parameters given strain measured at candidate sensor locations. These estimations are used to assess damage criticality, which is linked to buckling eigenvalues, and transformed into decisions. Bayesian optimization is used to select the candidates that minimize a utility function that considers the costs associated with making a certain decision plus the costs of acquiring and installing the SHM hardware (sensors, data acquisition system, etc.). The goal of the optimization is to find the sensor set that provides the lowest cost. The resulting optimal sensor configuration is presented, consisting of the number of sensors to be deployed and their respective locations. Finally, this work analyzes the performance achieved by several different cost functions, highlighting the importance of defining an objective function that reflects the goal of the SHM system, demonstrated to an aerospace application.