In this paper an algorithm for acoustic emission source localization in cylindrical shell structures is presented. The proposed algorithm is based on the propagation of uncertainty through the Unscented Transform. Time of arrival of desired wave modes and wave velocity are measured parameters, whose uncertainties are processed through the algorithm, which provides mean and covariance statistics for the predicted location. Results of the algorithm using the Unscented Transform are compared to a Monte Carlo simulation, and this is accomplished through the Kullback-Leibler divergence. The results support a strong correlation between the two, however, the Unscented Transform demonstrates superior computational speed.
Damage detection of pipeline systems is a tedious and time consuming job due to digging requirement, accessibility, interference with other facilities, and being extremely wide spread in metropolitans. Therefore, a real-time and automated monitoring system can pervasively reduce labor work, time, and expenditures. This paper presents the results of an experimental study aimed at monitoring the performance of full scale pipe lining systems, subjected to static and dynamic (seismic) loading, using Acoustic Emission (AE) technique and Guided Ultrasonic Waves (GUWs). Particularly, two damage mechanisms are investigated: 1) delamination between pipeline and liner as the early indicator of damage, and 2) onset of nonlinearity and incipient failure of the liner as critical damage state.
Proc. SPIE. 8692, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013
KEYWORDS: Atrial fibrillation, Sensors, Optical inspection, Structural health monitoring, Smart structures, Analytical research, Acoustic emission, Systems modeling, Expectation maximization algorithms, Picture Archiving and Communication System
Reinforced Concrete (RC) has been widely used in construction of infrastructures for many decades. The cracking behavior in concrete is crucial due to the harmful effects on structural performance such as serviceability and durability requirements. In general, in loading such structures until failure, tensile cracks develop at the initial stages of loading, while shear cracks dominate later. Therefore, monitoring the cracking modes is of paramount importance as it can lead to the prediction of the structural performance. In the past two decades, significant efforts have been made toward the development of automated structural health monitoring (SHM) systems. Among them, a technique that shows promises for monitoring RC structures is the acoustic emission (AE). This paper introduces a novel probabilistic approach based on Gaussian Mixture Modeling (GMM) to classify AE signals related to each crack mode. The system provides an early warning by recognizing nucleation of numerous critical shear cracks. The algorithm is validated through an experimental study on a full-scale reinforced concrete shear wall subjected to a reversed cyclic loading. A modified conventional classification scheme and a new criterion for crack classification are also proposed.
This paper proposes an adaptive Unscented Kalman Filter (UKF) algorithm for Acoustic Emission (AE) source
localization in plate-like structures in noisy environments. Overall, the proposed approach consists of four main stages:
1) feature extraction, 2) sensor selection based on a binary hypothesis testing, 3) sensor weighting based on a well-defined
weighting function, and 4) estimation of the AE source based on the UKF. The performance of the proposed
algorithm is validated through pencil lead breaks performed on an aluminum plate instrumented with a sparse array of
piezoelectric sensors. To simulate highly noisy environment, two piezoelectric transducers have been used to continually
generating high power white noise during testing.
This paper presents a method for Acoustic Emission (AE) source localization in isotropic plate-like structures based on
the Extended Kalman Filter (EKF). The accuracy of the traditional triangulation methods depends on the time of flight
(TOF) measurements and on the group velocity assumption so uncertainties in both should be taken into account and
filtered out. An algorithm based on the Extended Kalman Filter (EKF), capable of filtering out these uncertainties, has
been developed for the estimation: 1) the AE source location and 2) the wave velocity. Experimental tests have been
carried out on an aluminum plate to show accuracy and robustness of the proposed approach.