Journal ArticleDOI
Phased acoustic emission sensor array for localizing radial and axial positions of defects in hollow structures
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TLDR
In this paper, a phase array acoustic emission (AE) localization strategy is introduced to localize both radial and axial positions of defects by considering the actual trajectory of propagating elastic waves.About:
This article is published in Measurement.The article was published on 2020-02-01. It has received 17 citations till now. The article focuses on the topics: Acoustic emission & Sensor array.read more
Citations
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Introduction to acoustic emission
TL;DR: In this paper, the authors described typical acoustic emission signal characteristics and techniques which localize the signal source by processing the acoustic delay data from multiple sensors are discussed, which includes sensors, amplifiers, pulse counters, a minicomputer and output devices.
Journal ArticleDOI
Defect localization on rolling element bearing stationary outer race with acoustic emission technology
TL;DR: A fault localization method to obtain the defect position on a bearing based on the spatial localization of acoustic emission (AE) is proposed and the results demonstrate that this method can locate AE sources.
Journal ArticleDOI
The characterization of deformation stage of metals using acoustic emission combined with nonlinear ultrasonics
TL;DR: In this article, a new acoustic emission (AE) characteristic was introduced to determine the deformation stage of metals under quasi-static loading, which was extracted from the derivative of the cumulative AE energy with respect to time.
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Experimental Research on Rapid Localization of Acoustic Source in a Cylindrical Shell Structure without Knowledge of the Velocity Profile.
TL;DR: In this paper, the L-shaped sensor cluster technique is applied to the cylindrical surface of the pressure vessel, and the experimental results prove the applicability of using this technique.
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Acoustic Emission Intelligent Identification for Initial Damage of the Engine based on Single Sensor
TL;DR: In this article , the authors proposed a single-sensor acoustic emission (AE) diagnosis method based on deep learning (DL) for structural health monitoring (SHM) and initial damage intelligent identification of the engine.
References
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Journal ArticleDOI
An acoustic emission based multi-level approach to buried gas pipeline leakage localization
TL;DR: In this paper, a leakage localization approach based on the multi-level framework is proposed, which consists of two steps: regional localization and precise localization, and the results indicate that the accuracy of regional localization is 100% and the maximum error percentage of precise localization is 5.3% under varying sensors distances from 10 to 33m.
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Acoustic monitoring of pipeline flows: particulate slurries
TL;DR: In this paper, a case study of the use of a non-intrusive passive acoustic sensor for on-line monitoring of flow and related process parameters is presented, where the sensor was mounted externally on a small diameter pipeline conveying dense slurries of fine silica particles.
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Location of acoustic emission sources generated by air flow
TL;DR: This article describes one-dimensional location of continuous acoustic emission sources by using an intelligent locator, and shows that it is possible to determine an accurate location by using a combination of a cross-correlation function with an appropriate bandpass filter.
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Wavelet based harmonics decomposition of ultrasonic signal in assessment of plastic strain in aluminum
TL;DR: In this article, two wavelet-based algorithms are introduced to analyze the waveform in nonlinear ultrasonic testing and the results showed that the acoustic nonlinearity parameter calculated through the proposed algorithms is not influenced by signal processing variables, and the signal processing error is reduced when the waveletbased decomposition is applied.
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Survey of advances and challenges in intelligent autonomy for distributed cyber-physical systems
TL;DR: In this article, the authors explore the existing state-of-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare.