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Journal ArticleDOI

Industrial Noise Source Identification by Using an Acoustic Beamforming System

M. R. Bai, +1 more
- 01 Apr 1998 - 
- Vol. 120, Iss: 2, pp 426-433
TLDR
In this article, a noise source identification technique is proposed for industrial applications by using a microphone array and beamforming algorithms, both of the directions and the distances of long-range noise sources are calculated.
Abstract
A noise source identification technique is proposed for industrial applications by using a microphone array and beamforming algorithms. Both of the directions and the distances of long-range noise sources are calculated. The conventional method, the minimum variance (MV) method, and the multiple signal classification (MUSIC) method are the main beamforming algorithms employed in this study. The results of numerical simulations and field tests indicate the effectiveness of the acoustic beamformer in identifying noise sources in industrial environments.

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Citations
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Journal ArticleDOI

Source identification system based on the time-domain nearfield equivalence source imaging: Fundamental theory and implementation

TL;DR: In this paper, a nearfield equivalence source imaging (NESI) technique is proposed to identify locations and strengths of noise sources, which is based on the time-domain formulation that applies not only to stationary but also a transient noise.
Journal ArticleDOI

Optimized microphone deployment for near-field acoustic holography: To be, or not to be random, that is the question

TL;DR: In this article, numerical simulations are undertaken to optimize the microphone deployment for both far-field and near-field arrays with the latter being the main focus, and the simulation results suggest that the optimal near field array is the uniform rectangular array (URA) and the random deployment presents no particular benefit in near field imaging.
Journal ArticleDOI

Diesel engine noise source identification based on EEMD, coherent power spectrum analysis and improved AHP

TL;DR: An integrated noise source identification method based on the ensemble empirical mode decomposition (EEMD), the coherent power spectrum analysis, and the improved analytic hierarchy process (AHP) is presented.
Journal ArticleDOI

Data processing and augmentation of acoustic array signals for fault detection with machine learning

TL;DR: The proposed data processing methods are able to determine the position of the disturbance mass even with low amounts of training data and show to be promising for applications where space-frequency information is of essence.
Journal ArticleDOI

Three-dimensional localization of point acoustic sources using a planar microphone array combined with beamforming.

TL;DR: A beamforming-based acoustic imaging method employing a two-dimensional microphone array that not only can locate an acoustic source in the XY plane parallel to the array, but can also identify the distance between the source and array in the Z direction, denoted as the source depth, and thus provides three-dimensional (3D) localization ability.
References
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Journal ArticleDOI

Multiple emitter location and signal parameter estimation

TL;DR: In this article, a description of the multiple signal classification (MUSIC) algorithm, which provides asymptotically unbiased estimates of 1) number of incident wavefronts present; 2) directions of arrival (DOA) (or emitter locations); 3) strengths and cross correlations among the incident waveforms; 4) noise/interference strength.
Journal ArticleDOI

High-resolution frequency-wavenumber spectrum analysis

TL;DR: In this article, a high-resolution frequency-wavenumber power spectral density estimation method was proposed, which employs a wavenumber window whose shape changes and is a function of the wave height at which an estimate is obtained.
Book

Fundamentals of acoustics

TL;DR: In this article, the authors present a two-dimensional wave equation and simple solutions for the wave equation with respect to the two dimensions of the wave and the two types of vibrations.
Journal ArticleDOI

On spatial smoothing for direction-of-arrival estimation of coherent signals

TL;DR: An analysis of a "spatial smoothing" preprocessing scheme, recently suggested by Evans et al., to circumvent problems encountered in direction-of-arrival estimation of fully correlated signals.
Book

Array Signal Processing

TL;DR: The author explains the development of the Wiener Solution and some of the techniques used in its implementation, including Optimum Processing: Steady State Performance and theWiener Solution, which simplifies the implementation of the Covariance Matrix.
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