Graduate Thesis Or Dissertation

 

Lamb Wave Mode Decomposition and Its Applications in Structural Health Monitoring 公开 Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/k930c337p

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  • Structural health monitoring (SHM) systems perform automated non-destructive damage detection and characterization for a variety of large structures including civil structures such as bridges and aerospace structures such as aircrafts and space vehicles. The goals of SHM include preventing catastrophic structural failures, increasing reliability, reducing maintenance costs, and increasing the useful life span of the structures monitored by the systems. Guided waves have been extensively studied as means for monitoring the state of large structures. Guided waves, such as Lamb waves, propagate within the thickness of the structure, and are sensitive to damage. This makes them attractive for use in SHM systems. However, they are characterized by complex propagation characteristics such as dispersion, and multimodal and frequency-dependent attenuation, which often complicate analysis. In my dissertation research, we developed and evaluated four important components of a reliable guided wave-based SHM system for aerospace structures made out of composite materials and metals. These are: 1. A cross Wigner-Ville distribution-based mode decomposition algorithm to separate overlapped modes in sensor signals. Separating the mode components in sensor signals has several applications in SHM. Algorithms (2) and (3) are two examples where separated mode components are used. 2. A sparse tomographic reconstruction algorithm based on decomposed mode components to estimate the extent of damage on the structure. Estimating the extent of damage allows us to reliably predict the remaining useful life of the structure. The anomaly-imaging algorithm estimates damage extent with accuracies comparable to manual ultrasonic inspection techniques such as C-scan when the sensor density is sufficiently high. 3. An algorithm to compensate for the effect of temperature on sensor signals. The damage characterization algorithm developed in (2) requires a set of baseline signals recorded on the structure before the introduction of damage. Temperature changes can introduce changes in sensor signals that maybe interpreted as damage. The temperature compensation algorithm will mitigate difficulties caused by such changes in sensor signals. 4. A baseline-free damage detection algorithm from sensor signals under varying environmental conditions. Baseline comparison methods for SHM in time-varying environments require training on data recorded from damaged structures. The baseline-free damage detection algorithm overcomes this challenge. The algorithm is trained using only signals acquired from the damage-free structures. The four algorithms presented in this dissertation have the potential to form the basis for the next generation of SHM systems for aerospace structures and provide unprecedented accuracy in terms of detecting damage and estimating its extent for better residual structural analysis. Such a system will facilitate safer air travel. In addition, it will hasten the transition from currently employed schedule-based maintenance to a condition-based maintenance strategy resulting in less downtime time and reduced maintenance costs for aerospace structures.
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