Deconvolution of acoustic emissions for source localization using time reverse modeling

2017 ◽  
Vol 387 ◽  
pp. 66-78 ◽  
Author(s):  
Georg Karl Kocur
Keyword(s):  
Source Localization ◽  
Acoustic Emissions

scholarly journals Multichannel Detection of Acoustic Emissions and Localization of the Source with External and Internal Sensors for Partial Discharge Monitoring of Power Transformers

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7873
Author(s):  
Iago Búa-Núñez ◽  
Julio E. Posada-Román ◽  
José A. García-Souto
Keyword(s):  
Source Localization ◽  
Error Propagation ◽  
Detection System ◽  
Back Propagation ◽  
Acoustic Emissions ◽  
Power Transformers ◽  
Programming System ◽  
Localization Algorithms ◽  
On Line ◽  
The Times

The detection of acoustic emissions with multiple channels and different kinds of sensors (external ultrasound electronic sensors and internal optical fiber sensors) for monitoring power transformers is presented. The source localization based on the times of arrival was previously studied, comparing different strategies for solving the location equations and the most efficient strategy in terms of computational and complexity costs versus performance was selected for analyzing the error propagation. The errors of the acoustic emission source location (localization process) are evaluated from the errors of the times of arrival (detection process). A hybrid programming architecture is proposed to optimize both stages of detection and location. It is formed by a virtual instrumentation system for the acquisition, detection and noise reduction of multiple acoustic channels and an algorithms-oriented programming system for the implementation of the localization techniques (back-propagation and multiple-source separation algorithms could also be implemented in this system). The communication between both systems is performed by a packet transfer protocol that allows continuous operation (e.g., on-line monitoring) and remote operation (e.g., a local monitoring and a remote analysis and diagnosis). For the first time, delay errors are modeled and error propagation is applied with this error source and localization algorithms. The 1% mean delay error propagation gives an accuracy of 9.5 mm (dispersion) and a maximum offset of 4 mm (<1% in both cases) in the AE source localization process. This increases proportionally for more severe errors (up to 5% reported). In the case of a multi-channel internal fiber-optic detection system, the resulting location error with a delay error of 2% is negligible when selecting the most repeated calculated position. These aim at determining the PD area of activity with a precision of better than 1% (<10 mm in 110 cm).

scholarly journals Optimal Passive Source Localization for Acoustic Emissions

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1585
Author(s):  
Carlos A. Prete ◽  
Vítor H. Nascimento ◽  
Cássio G. Lopes
Keyword(s):  
Source Localization ◽  
Sampling Rate ◽  
Acoustic Emissions ◽  
Random Variable ◽  
Low Noise ◽  
Source Position ◽  
Fixed Threshold ◽  
Noisy Signals ◽  
Passive Source Localization ◽  
Threshold Technique

Acoustic emission is a non-destructive testing method where sensors monitor an area of a structure to detect and localize passive sources of elastic waves such as expanding cracks. Passive source localization methods based on times of arrival (TOAs) use TOAs estimated from the noisy signals received by the sensors to estimate the source position. In this work, we derive the probability distribution of TOAs assuming they were obtained by the fixed threshold technique—a popular low-complexity TOA estimation technique—and show that, if the sampling rate is high enough, TOAs can be approximated by a random variable distributed according to a mixture of Gaussian distributions, which reduces to a Gaussian in the low noise regime. The optimal source position estimator is derived assuming the parameters of the mixture are known, in which case its MSE matches the Cramér–Rao lower bound, and an algorithm to estimate the mixture parameters from noisy signals is presented. We also show that the fixed threshold technique produces biased time differences of arrival (TDOAs) and propose a modification of this method to remove the bias. The proposed source position estimator is validated in simulation using biased and unbiased TDOAs, performing better than other TOA-based passive source localization methods in most scenarios.

Oto Acoustic Emissions (OAE): the shape-similarity of pure-tone audiogram and OAE-gram

1999 ◽  
Vol 24 (4) ◽  
pp. 377-383
Author(s):  
G. A. van zanten ◽  
A. van de sande ◽  
M. P. brocaar
Keyword(s):  
Pure Tone ◽  
Acoustic Emissions ◽  
Shape Similarity ◽  
Pure Tone Audiogram
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