Researchers conventionally employ thermal imaging to monitor the health of animals, observe their habitat utilization, and track their activity patterns. These non-invasive methods can generate detailed images and offer valuable insights into behavior, movements, and environmental interactions. The aye-aye (Daubentonia madagascariensis), a rare and endangered lemur from Madagascar, possesses a uniquely slender third finger evolved for tapping surfaces at relatively high frequencies. The adaptation enables acoustic-based sensing to locate cavities with prey in trees to enhance their foraging abilities. The authors’ previous studies have demonstrated some descent simulating dynamic models of the aye-aye’s third digit referenced from limited data collected with monocular cameras, which can be challenging due to noisy and distorted images, impacting motion analysis adversely. In this proposed research, high-speed thermal cameras are employed to capture detailed finger position and orientation, providing a clearer understanding of the overall dynamic range. The improved biomimetic model aims to enhance tap-testing strategies in nondestructive evaluation for various inspection applications.
The aye-aye (Daubentonia madagascariensis) is the largest nocturnal primate in the world and possesses a number of distinct adaptations. The most striking feature of the aye-aye is perhaps its exceptional near-field auditory system adopted to support its unique tap-scanning process. This tap-scanning technique represents prominent evolutionary innovations in the animal’s biological auditory system. The current study provides an initial insight into proposing a biomimetic approach to determine how different morphological features might impact the ayeaye’s acoustic near-field auditory system. The experimental setup comprised a miniature piezoelectric hammer mounted on a Universal Robotics manipulator (UR5) (the integrated system provides a controlled tapping process) and a prepolarized capacitive measurement microphone (to capture the acoustic sound coming from each tap on the wooden sample). The pinnae of the aye-aye were 3D printed using a CT scan obtained from a carcass. The results show that the biomimetic setup can successfully be used for evaluating the near-field auditory system of aye-ayes.
The aye-aye (Daubentonia madagascariensis) is a nocturnal lemur native to the island of Madagascar with a special thin middle finger. The aye-aye’s third digit (the slenderest one) has a remarkably specific adaptation, allowing it to perform tap-scanning (Finger tapping) to locate small cavities beneath tree bark and extract woodboring larvae from it. This finger, as an exceptional active acoustic actuator, makes an aye-aye’s biological system an attractive model for Nondestructive Evaluation (NDE) methods and robotic systems. Despite the important aspects of the topic in engineering sensory and NDE, little is known about the mechanism and movement of this unique finger. In this paper a simplified kinematic model was proposed to simulate the aye-aye’s middle finger motion.
This paper presents phase-space analysis of nonlinear ultrasound in concrete materials subjected to different compression loads. Nonlinearities due to defects and material properties can alter frequency content of transmitted and received ultrasound waveforms. As a result, nonlinear ultrasound waveforms are traditionally analyzed in frequency domain. However, using frequency domain to analyze ultrasound behavior has several shortcomings. Different sources of nonlinearities can make the same change in the frequency domain. This can make the identification of the source of ultrasound nonlinearities impossible. In addition, it is hard to observe and explain complex nonlinearities such as chaotic behavior of ultrasound waveforms in the frequency domain. Analyzing nonlinear ultrasound behavior in phase-space domain allows for a better understanding of ultrasound behavior. Fractal analysis of phase space portrait is used to quantitatively evaluate topography of phase space portrait of ultrasound waveforms. Fractal analysis is a quantitative feature to describe geometry evolution and it can enhance quantitative analysis of phase space portrait. The phase-space along with fractal analysis is proven to be a powerful tool in analyzing ultrasound nonlinearities and determining chaotic behavior.
In the last decade, Lamb wave based structural health monitoring (SHM) has been gaining attention for real-time monitoring of plate-like structures. Although the capabilities of Lamb wave in detecting cracks in metallic plates have been studied and proven, there is not a systematic method explaining how to predict network coverage of a sparse array of sensors to send and receive Lamb waves. In this study, initial steps are taken to simulate and predict coverage area of simple sensor network configurations for detecting a specific type of under-surface crack. Computer models are developed for Lamb wave propagation simulation using Finite Element Method (FEM). Using the models, an initial framework is developed to estimate coverage area of a sensor network to detect one type of under-surface crack. Using the framework, coverage area of three different simple sensor arrangements to detect horizontal cracks in an aluminum plate is estimated. The first arrangement is a single transducer, whereas the two other arrangements consist of two and three transducers/sensors respectively. Coverage area is increased as number of transducers in the sensor network is increased.
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.
KEYWORDS: Structural health monitoring, Acoustic emission, Sensors, Expectation maximization algorithms, Atrial fibrillation, Systems modeling, Optical inspection, Picture Archiving and Communication System, Smart structures, Analytical research
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.
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