Guided waves for the health monitoring of sign support structures under varying environmental conditions. Issue 2 (22nd August 2011)

• Record Type: Journal Article
• Authors: Zhu, Xuan; Rizzo, Piervincenzo
• Is Part Of: Structural control and health monitoring. Volume 20:Issue 2(2013:Mar.)
• Page Start: 36
• Page End: 52
• Publication Date: 2011-08-22
• Subjects: Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.481
• Languages: English
• ISSNs: 1545-2255

Abstract

<abstract abstract-type="main"> <title>SUMMARY</title> <p>Guided ultrasonic waves are increasingly used in all those structural health monitoring applications that benefit from built‐in transduction, moderately large inspection ranges, and high sensitivity to small flaws. The wave‐based structural health monitoring of complex or thick structures can be difficult because of factors such as simultaneous propagation of many modes, large number of overlapping reflections, and mode conversion. Moreover, if the structures are subjected to environmental changes, variations on the characteristics of the detected signals are observed. This paper describes a health monitoring system for large trusses that combines the advantages of guided ultrasonic waves with the extraction of defect‐sensitive features aimed at performing a multivariate diagnosis of damage. The system was tested on a chord of a dismantled overhead sign support structure subjected to changing environmental conditions. Cold/warm temperatures and wet/snow boundary conditions were created. The system's hardware consisted of a data acquisition unit that controlled the generation and detection of ultrasonic signals by means of an array of piezoelectric transducers. The signals were processed using a feature‐based approach whereby features between two signals collected simultaneously or between a signal and a baseline were compared. The features were then fed into an unsupervised learning algorithm based on outlier analysis. Experimental results show that damage can be detected even in the presence of temperature and boundary condition variations. Copyright © 2011 John Wiley & Sons, Ltd.</p> </abstract>