Guided waves for the health monitoring of sign support structures under varying environmental conditions

Abstract

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 wavebased 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.

Guided ultrasonic waves for the health monitoring of existing sign support structures

“…Overall the SHM paradigm presented here and elsewhere [12] seems to be adequate to the detection of damage that is relatively small compared to the size and the complexity of the structure. Improvement should be made in order to mitigate the effects of variable boundary conditions as the monitoring algorithm underperforms when the boundary conditions are not uniform.…”

“…Then the peak-to-peak (ppk) amplitude, variance (var), root mean square (rms), crest factor (cf), and k-factor (kf) were calculated for each time-windowed signal. These features were selected based upon the outcomes of previous researches of the authors' group [1,2,12].…”

“…A damage index was constructed by the ratio of a certain feature values taken from two wave paths symmetric with respect to the joint [1,2,12]. The hypothesis is that, irrespective of the environmental and boundary conditions that are assumed identical for both paths, the characteristics of the corresponding time-histories will vary once one of the two segments develops structural anomalies.…”

“…Such variability needs to be identified and minimized to the extent possible [5]. For GUW-based SHM, temperature is the most common environmental factor investigated and several researches predicted the effect of temperature on the propagation of GUWs and proposed signal processing strategies to mitigate these effects [6][7][8][9][10][11][12]. In this paper we present the results of three experiments, one carried out in the laboratory and two carried out in the field.…”

Recent progress and future trends on damage identification methods for bridge structures

“…Laory et al evaluated damage detectability and detection time of two model‐free damage detection methods, that is, the moving principal component analysis method and the robust regression analysis method, considering the temperature effect; the evaluation is conducted in a numerical analysis based on a railway truss bridge in Germany, and three numerical damage cases were simulated in the form of stiffness losses at the top and bottom chords of the truss bridge model; results show that the two methods are complementary and have different seasonal variation sensitivities. Zhu and Rizzo presented a damage detection method for truss welded joints based on guided ultrasonic waves; cracks of various sizes near the weld toe of a joint in an experimental truss are simulated under varying environmental effects; it is found that the combination of damage features can increase the damage sensitivity and decrease environmental effects including the varying temperature and the boundary condition.…”