Optimal Fiber-Optic Sensor Placement Framework for Structural Health Monitoring of an Aircraft’s Wing Spar

In this work, an optimal fiber-optic sensor placement framework is proposed for structural health monitoring (SHM) of an aircraft’s wing spar. The spar is entirely made of composite materials and the damage of interest is debonding between laminates. A high-fidelity finite element (FE) model is used as a synthetic data generator for this problem. The inputs are debonding damage size and location, and the outputs are uniaxial strain measurements. To overcome the high computational costs of each FE evaluation, “run time” surrogate models are created using different machine learning methods. Then, Bayesian inference is used to estimate the damage parameters given strain measured at candidate sensor locations. These estimations are used to assess damage criticality and transformed into decisions. The Bayesian optimization uses a utility function that selects the most valuable candidates by maximizing the expected information gain between prior and posterior probability distributions of the estimated damage parameters. The total cost of the design is calculated for each candidate sensor array, consisting of the costs associated with making a certain decision plus the costs of acquiring and installing the SHM hardware (sensors, data acquisition system, etc.). The candidate with the lowest cost is selected. The resulting optimal sensor configuration is presented.