Proc. SPIE. 5394, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems III
KEYWORDS: Actuators, Ferroelectric materials, Sensors, Signal attenuation, Wave propagation, Signal processing, Structural health monitoring, Aluminum, Microsoft Foundation Class Library, Time-frequency analysis
This paper illustrates an integrated approach for identifying structural damage in an aluminum plate. Piezoelectric (PZT) materials are used to actuate/sense the dynamic response of the structure. Two damage identification techniques are integrated in this study, including Lamb wave propagations and impedance methods. In Lamb wave propagations, one PZT launches an elastic wave through the structure, and responses are measured by an array of PZT sensors. The changes in both wave attenuation and reflection are used to detect and locate the damage. The impedance method monitors the variations in structural mechanical impedance, which is coupled with the electrical impedance of the PZT. Both methods operate in high frequency ranges at which there are measurable changes in structural responses even for incipient damage such as small cracks or loose connections. This paper summarizes two methods used for damage identification, experimental procedures, and additional issues that can be used as a guideline for future investigations.
Recently, a new approach in vibration-based structural health monitoring has been developed utilizing features extracted from concepts in nonlinear dynamics systems theory. The structure is excited with a low-dimensional chaotic input, and the steady-state structural response attractor is reconstructed using a false nearest neighbors algorithm. Certain features have been computed from the attractor such as average local "neighborhood" variance, and these features have been shown in previous works to exceed the damage resolving capability of traditional modal-based features in several computational and experimental studies. In this work, we adopt a similar attractor approach, but we present a feature based on nonlinear predictive models of evolving attractor geometry. This feature has an advantage over previous attractor-based features in that the input excitation need not be monitored. We apply this overall approach to a steel frame model of a multi-story building, where damage is incurred by the loosening of bolted connections between model members.