This article introduces a novel approach to damage detection in the context of a structural health monitoring system. A statistical model of the ultrasonic guided wave interrogation process was developed and used to formulate a likelihood ratio test. From the likelihood ratio test, the optimal detector for distinguishing damaged and undamaged states of the structure was derived and found to be a metric related to signal energy. That result was confirmed using a data-driven approach based on using receiver-operating characteristic curves to compare the energy metric to other metrics found in the literature. The data in this study were generated from instrumenting two separate, geometrically complex structures with ultrasonic sensor-actuators. A three-story bolted-frame structure was constructed in the laboratory to test the approach for connections that produce highly uncertain wave paths, with damage being introduced through local impedance changes and bolt loosening. The second test structure was a section of a fuselage rib taken from a commercial aircraft in which holes and cracks were introduced to provide a testbed with a high degree of realism. The detection performance in both structures was quantified and presented. Finally, different sensor fusion strategies were implemented and the ability of such techniques to increase the statistical distinction between damaged and undamaged cases was quantified.
Ultrasonic guided waves represent a promising technique for detecting and localizing structural damage, but their application to realistic structures has been hampered by the complicated interference patterns produced by scattering from geometric features. This work presents a new damage localization paradigm based on a statistical approach to dealing with uncertainty in the guided wave signals. A bolted frame and a section of a fuselage rib are tested with different simulated damage conditions and used to conduct a detailed comparison between the proposed solution and other sparse-array localization approaches. After establishing the superiority of the statistical approach, two novel innovations to the localization procedure are proposed: an approach to sensor fusion based on the Neyman-Pearson criterion, and a method of constructing simple models of geometrical features. Including the sensor fusion and geometrical models produces a substantial improvement in the system's localization accuracy. The final result is a robust and accurate framework for single-site damage localization that moves structural health monitoring towards practical implementation on a much broader range of structures.
Bearing damage in composite bolted connections (such as those commonly used in aerospace applications) is investigated through the application of ultrasonic guided waves. Specifically, identical macro fiber composite sensor arrays were bonded to each of two identical composite plates. One plate was then inspected ultrasonically in the unloaded condition with bearing damage being introduced through successive uniaxial tensile tests, while the other plate was assessed while under load in the tensile testing machine. A scattering matrix approach is employed to characterize the interaction of the guided waves with the target bolt hole. The effects of applied load and the bolt fixture on the ultrasonic results are explored. A parametric study is carried out to determine the optimal actuation frequency and interrogation angle for this application. The results demonstrate that the system is capable of detecting bolt bearing damage as well as monitoring of the applied load in the elastic region.
Using full-field ultrasonic guided wave data can provide a wealth of information on the state of a structure through a detailed characterization of its wave propagation properties. However, the need for appropriate feature selection and quantified metrics for making rigorous assessments of the structural state is in no way lessened by the density of information. In this study, a simple steel bolted connection with two bolts is monitored for bolt loosening. The full-field data were acquired using a scanning-laser-generated ultrasound system with a single surface-mounted sensor. Such laser systems have many advantages that make them attractive for nondestructive evaluation, including their high-speed, high spatial resolution, and the ability to scan large areas of in-service structures. In order to characterize the relationship between bolt torque and the resulting wavefield in this specimen, the bolt torque in each of the bolts is independently varied from fully tightened to fully loosened in several steps. First, qualitative observations about the changes in the wavefield are presented. Next, an approach to quantifying the wave transmission through the bolted joint is discussed. Finally, a method of monitoring the bolt torque using the ultrasonic data is demonstrated.
This paper presents the development of a Bayesian framework for optimizing the design of a structural health monitoring (SHM) system. Statistical damage detection techniques are applied to a geometrically-complex, three-story structure with bolted joints. A sparse network of PZT sensor-actuators is bonded to the structure, using ultrasonic guided waves in both pulse-echo and pitch-catch modes to inspect the structure. Receiver operating characteristics are used to quantify the performance of multiple features (or detectors). The detection rate of the system is compared across different types and levels of damage. A Bayesian cost model is implemented to determine the best performing network.
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