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Acoustic Imaging

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Habib Ammari – One of the best experts on this subject based on the ideXlab platform.

Wei Fan – One of the best experts on this subject based on the ideXlab platform.

  • experimental study on underwater Acoustic Imaging of 2 d temperature distribution around hot springs on floor of lake qiezishan china
    Experimental Thermal and Fluid Science, 2010
    Co-Authors: Wei Fan, Ying Chen, Huachen Pan, Yong Cai, Zhujun Zhang
    Abstract:

    Abstract An experimental study is performed on Acoustic Imaging of underwater temperature fields in Lake Qiezishan, located at Longling Country, Yunnan Province, China. There are hot springs and craters beneath Lake Qiezishan, which lies at 24°32′33″ north latitude and 98°47′44″ east longitude. Using the Acoustic method, the temperature fields are recovered with the computed tomography of the measured time-of-flight data. Regarding the measuring system, hydrophones are used as transmitters and receivers. The signal correlation analysis is performed to obtain accurate Acoustic wave transit time from the transmitted and received Acoustic signals. Both the transmitter emission start time and the receiver capture start time are also estimated using a novel algorithm based on the time-of-flight measurements at different water temperature. The reconstruction of the 2-D temperature field which is an ill-posed problem, is conducted by total least-squares method. The reconstructed temperature distributions are rather approximations to the actual distributions measured with thermocouples. The preliminary results show that the potential of underwater Acoustic Imaging will be useful for the in situ monitoring of temperature distributions around sea-floor hydrothermal vents.

Jerome Antoni – One of the best experts on this subject based on the ideXlab platform.

  • Acoustic Imaging applied to fault detection on a rotating machine bench
    Proceedings of Isma, 2016
    Co-Authors: E Cardenas Cabada, Nacer Hamzaoui, Quentin Leclere, Jerome Antoni
    Abstract:

    One of the principal application of Acoustic Imaging techniques is to identify the Acoustic sources over a surface. The purpose of this study is to show the relevance of Acoustic Imaging as a fault detection tool. A rotating machine bench is used to generate experimentally bearings and gears faults configurations. A 45 microphones array with a spiral distribution is used to register the Acoustic radiation. Before applying the Acoustic Imaging technique, several operations are applied to the recorded signals. The signals are firstly angularly resampled thanks to an angle encoder. Secondly, the synchronous average is used on the signals in order to separate the synchronous and random parts with regard to the mechanism periodicity. Beamforming is then applied to both extracted signals in order to visualize the synchronous and the random Acoustic sources. The gears and shaft signals are theoretically synchronous with the mechanism frequency while bearing is not necessarily. The fault signature extracted can be visualized spatially thanks to beamforming.

  • Acoustic Imaging applied to fault detection in rotating machine
    , 2015
    Co-Authors: E Cardenas Cabada, Nacer Hamzaoui, Quentin Leclere, Jerome Antoni
    Abstract:

    Fault detection in rotating machines is generally based on vibration signal analysis. However, the sound radiated by a structure and its vibration are closely linked. We can therefore imagine that Acoustic measurements could be useful for diagnostic improvement. In this paper, a fault diagnostic method based on Acoustic Imaging is proposed. Beamforming is used to describe the Acoustic field generated by an operating machine. Usually, the source strength is mapped in order to identify the radiating areas. In this paper, fault detection features are plotted instead. The spectral kurtosis is mapped as a function of space and frequency to separate and localise impulsive sources.

  • Cylindrical cyclic Acoustic Imaging with a Bayesian approach for cyclostationary sources reconstruction.
    Journal of the Acoustical Society of America, 2013
    Co-Authors: Sébastien Personne, Jerome Antoni, Jean-daniel Chazot
    Abstract:

    Standard Acoustic Imaging techniques, such as beamforming or near Acoustical holography, are now widely used in engineering contexts. However, large arrays of microphones are sometimes required to have a good resolution. Besides new challenges arise, particularly in the field of non stationary sources, which need to be identified and solved. Cyclostationary sound sources, a specific kind of non stationary signals, are characterized by statistical properties evolving periodically in time. In practice, the first-order statistical properties contain some periodic components while the second orders may be random with a periodic flow of energy. The present work tackles the Acoustic Imaging of cyclostationary sources with a scanning microphone, i.e., without any array. Cylindrical surfaces, adapted to standard rotating machines, are considered. The reconstruction difficulty of Acoustic sources from discrete measurements is addressed here thanks to the cyclostationary properties. A cyclic sound field is hence extracted from the discrete measurements. Finally, a Bayesian formulation, gathering both physical and probabilistic information on this inverse problem, is used to back propagate the sound over the radiating surface.

Ning Chu – One of the best experts on this subject based on the ideXlab platform.

José Picheral – One of the best experts on this subject based on the ideXlab platform.

  • Convolution Models with Shift-invariant kernel based on Matlab-GPU platform for Fast Acoustic Imaging
    , 2014
    Co-Authors: Ning Chu, Nicolas Gac, José Picheral, Ali Mohammad-djafari
    Abstract:

    Acoustic Imaging is an advanced technique for Acoustic source localization and power reconstruc-tion from limited noisy measurements at microphone sensors. This technique not only involves in a forward model of Acoustic propagation from sources to sensors, but also its numerical solution of an ill-posed inverse problem. Nowadays, the Bayesian inference methods in inverse methods have been widely investigated for robust Acoustic Imaging, but most of Bayesian methods are time-consuming, and one of the reasons is that the forward model causes heavy matrix multiplication. In this paper, we focus on the acceleration of the forward model by using a 2D-invariant convo-lution and a separable convolution respectively; For hardware acceleration, the Matlab-Graphics Processing Unit application are discussed. For method validation, we use the simulated and real data from the wind tunnel experiment in automobile industry.

  • A hierarchical variational Bayesian approximation approach in Acoustic Imaging
    , 2014
    Co-Authors: Ning Chu, Ali Mohammad-djafari, Nicolas Gac, José Picheral
    Abstract:

    Acoustic Imaging is a powerful technique for Acoustic source localization and power reconstruction from limited noisy measurements at microphone sensors. But it inevitably con-fronts a very ill-posed inverse problem which causes unexpected solution uncertainty. Recently, the Bayesian inference methods using sparse priors have been effectively investigated. In this paper, we propose to use a hierarchical variational Bayesian approximation for robust Acoustic Imaging. And we explore the Student-t priors with heavy tails to enforce source sparsity, and to model non-Gaussian noise respectively. Compared to conventional methods, the proposed approach can achieve the higher spatial resolution and wider dynamic range of source powers for real data from automo-bile wind tunnel.

  • A variational Bayesian approximation approach via a sparsity enforcing prior in Acoustic Imaging
    , 2014
    Co-Authors: Ning Chu, Ali Mohammad-djafari, Nicolas Gac, José Picheral
    Abstract:

    Acoustic Imaging is an advanced technique for Acoustic source localization and power reconstruction from limited noisy measurements at microphone sensors. To solve this ill-posed inverse problem, the Bayesian inference methods using proper prior knowledge have been widely investigated. In this paper, we propose to use a hierarchical Variational Bayesian Approximation for the robust Acoustic Imaging. And we explore the Student’s-t priors with heavy tails to enforce source sparsity and non-Gaussian noises, so that we can achieve the super spatial resolution and wide dynamic range of source powers. In addition, proposed approach is validated by simulations and real data from wind tunnel in automobile industry.