Identification of Anomaly in Structures Based on Locally Controlled Dynamic Inputs

点击翻译按钮获取中文摘要

AbstractCMS-0424141Chang, Fu-KuoStanford UniversityRapid and accurate detection of anomaly in structures while in service is major a challenge in engineering. Recent advances in sensor and smart materials technologies provide promising opportunities to overcome current time-consuming and labor-intensive inspection methods. The key to the success of the sensor-based technologies depends strongly on how the sensor measurements can be correlated with the physical quantity in terms of size and location of the anomaly. Although challenging, the mathematical complexity of the sensor-based systems becomes significantly reduced if the inputs to generate the data are well controlled. This leads to a fundamental mathematical issue: Given limited sensor data resulting from controlled inputs, identify local condition of an object.Therefore, an investigation is undertaken to develop a mathematical framework for detecting anomaly in structures using distributed sensor measurements generated from locally controlled dynamic excitations as well as appropriate algorithms to identify the location and size of the anomaly based on the measurements. Both analytical and experimental work will be conducted during the investigation. The major tasks to be performed for the study include: Diagnostic Signal Generation, Signal Interrogation and Interpretation, and Implementation and Verification. Although simple coupon tests will be used to verify the results, the mathematical framework is fundamental and shall allow engineers to explore new mathematical formulations for data interpretation of any complex systems. For instance, the framework could be applied to monitor fatigue cracks in aircraft structures, to detect corrosion cracks for underground pipelines, to provide early warning of incipient failure in spacecraft, or to interrogate the integrity of bridges or buildings after major quakes. Furthermore, the interrogation algorithms will provide useful tools for readily applying sensing and monitoring techniques for a broad range of engineering fields.This project is supported by CMS under the Math-Eng Interfacing Initiative.