This research focuses on developing an embedded sensor system to monitor the health of a composite rotor component. To support this objective, simulations were developed to investigate the impact of sensor insertions on local structural micro-mechanics and sensor responses. In particular, the potential side-effects (e.g., delamination onset and growth) of imbedding lead zirconate titanate (PZT) piezoelectric sensors in composite structures were studied. A modeling approach for evaluating interlaminar damage under the influence of embedded PZT sensors is proposed. The approach uses finite element cohesive zone models to describe interlaminar damage between plies or at ply ends. In addition, an embedded multi-ply PZT model was developed and integrated with the damage models. The approach presented in this paper analyzes the propagation of interlaminar damage in the vicinity of sensors and quantifies the effect of sensor presence on damage growth. A parametric study was performed to understand how damage zones, the size and geometry of resin pockets, and the locations and properties of PZT sensors affected interfacial strength. Damage behavior, under the influence of an embedded PZT sensor, was examined in specimens having a configuration similar to that of a selected rotating rotorcraft component. Finally, optimal locations of embedded PZT transducers were determined for the specimen under consideration.
An integrated sensor system that continuously monitors the structural integrity of an aircraft’s critical composite components can have a high payoff by reducing risks, costs, inspections, and unscheduled maintenance, while increasing safety. Hybrid sensor networks combine or fuse different types of sensors. Optimal sensor fusion tries to find the optimal number and location of different types of sensors such that their combined probability of detection is maximized. Optimal hybrid sensor networks can be more robust, more accurate, and/or cheaper than networks consisting only of homogeneous sensors. A generic sensor fusion approach that combines the probabilities of detection of heterogeneous sensors is described. A fast greedy optimization approach that provides approximate solutions is described and demonstrated. Computable lower and upper bounds of a probability of detection objective function were determined. Fiber Bragg grating sensors can be inserted in layers of composite structures to provide local damage detection, while surface-mounted piezoelectric lead zirconate titanate sensors can provide global damage detection for the host structure under consideration. The generic approach is demonstrated on such combinations of fiber Bragg grating and lead zirconate titanate sensor networks. It is demonstrated that the proposed approach can be used to answer structural health monitoring network design problems such as the following: (1) Given a number of sensors, what is the maximum probability of detection that the sensors can attain and where should they be positioned to provide the maximum probability of detection? (2) If a given probability of detection is desired, the minimum number, types, and locations of sensors that are needed to attain this probability of detection can be determined. The approach is generic, that is, it can be extended to any number or types of sensors for which probabilities of detection can be defined.
Methods for constructing damage localization maps from networks of piezoelectric lead zirconate titanate (PZT) acoustic sensor measurements were investigated. A new approach, asymmetric acoustic Scattering (AAS), for damage detection and localization using PZT sensor arrays was developed, and the conclusions were favorable compared to commercial software results. The AAS method is simple, computationally efficient, and general because it does not require geometric details of the component or complex physical models. The approach estimates damage in five stages: (1) damage index (DI) measures are computed for individual sensors, (2) DI-weighted averages are computed for pairs of sensors, (3) DI-weighted averages are used to generate local DI maps, (4) local DI maps are combined into frequency-dependent global DI maps, and (5) frequencydependent DI maps are combined into global DI maps. The AAS algorithm generates temporal DI curves to increase the confidence of damage detection, whereas damage localization is accomplished with static distributed DI maps. Accuracy, robustness, and sensor array minimization methods for improving damage detection and localization were also investigated.
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