This work provides a system-level investigation into the use of embedded fiber Bragg grating optical sensors as a viable sensing architecture for the structural health monitoring of composite structures. The practical aspects of the embedding process are documented for both carbon fiber-reinforced polymer and glass fiber-reinforced polymer structures manufactured by both oven vacuum bag and vacuum-assisted resin transfer method processes. Initially, embedded specimens were subject to long-term water submersion to verify performance in an underwater environment. A larger, more complex jointed specimen was also fabricated with a fully embedded sensor network of fiber Bragg gratings and subjected to incrementally induced bearing damage. Using commercially available interrogation hardware, a damage detection structural health monitoring algorithm was developed and deployed. The results permit statistically precise detection of low levels of connection damage in the composite specimen.

In this study, a damage detection and localization scenario is presented for a composite laminate with a network of embedded fiber Bragg gratings. Strain time histories from a pseudorandom simulated operational loading are mined for multivariate damage-sensitive feature vectors that are then mapped to the Mahalanobis distance, a covariance-weighted distance metric for discrimination. The experimental setup, data acquisition, and feature extraction are discussed briefly, and special attention is given to the statistical model used for a binary hypothesis test for damage diagnosis. This article focuses on the performance of different estimations of the Mahalanobis distance metric using robust estimates for location and scatter, and these alternative formulations are compared to traditional, less robust estimation methods.

This work presents a new method for monitoring the preload torque in a composite bolted connection using an embedded fiber Bragg grating sensor. A unique washer was designed to impose a specified nonuniform strain field across the grating, causing distortion in the reflected optical spectrum. Using the full-width at half maximum bandwidth of the Bragg reflection spectrum as the preload-sensitive feature, it is shown that this feature increases monotonically-and quite linearly-with increasing applied bolt torque. It is also demonstrated that, although distorted, the spectral structure of the sensor is maintained such that it is simultaneously able to function in its typical use as a uniaxial strain sensor, thus essentially creating a dual-purpose sensor. The computational design approach is validated with a prototype experiment.

No abstract

In recent years, the use of composite materials has helped achieve ever-increasing performance requirements in marine, aerospace, and civil structures. A parallel interest in the structural health monitoring (SHM) of composites has developed to further improve performance by reducing overall lifecycle costs. In this work, a network of embedded fiber Bragg gratings (FBG) is employed as part of a damage detection system for an impact damage scenario in a composite laminate material system. Delamination damage is incrementally introduced into the laminate via repeated impacts with a drop weight pendulum system. Using vibration time histories between impacts from a simulated, pseudorandom operational loading, damage sensitive features were extracted and placed within a Mahalanobis distancebased discrimination framework. The statistical modeling for hypothesis testing is also presented to give a full, systems-level approach to a damage detection system from data acquisition to ultimate decision making.