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The Modal Composition Beamforming (MCB) method can quickly achieve the localization of fast-rotating sound sources; however, its low resolution and unclear applied conditions severely limit its industrial application. This study aims to investigate the MCB-based high-resolution localization method for multiple rotating sound sources and apply it to the localization and identification of axial-fan blade-noise. In this paper, the Variational Bayesian Approximation (VBA) and the Subspace Variational Bayesian Approximation (SVB) methods are used to solve the MCB-based rotating sound power propagation (RSP) model, denoted as RSP-VBA and RSP-SVB, respectively. The effectiveness of the proposed RSP-VBA and RSP-SVB are experimentally validated for the first time. Compared to the conventional MCB method, the resolution is significantly improved, and the proposed high-resolution methods are even faster than the classical Rotating Source Identifier (ROSI) method in most conditions. More importantly, the applied condition of the MCB, RSP-VBA, and RSP-SVB methods is given and verified by using three evaluation indicators. Then a schematic of the applied condition with examples is provided. With the guide of the applied conditions, the RSP-VBA and RSP-SVB methods are applied to the blade-noise localization of various multiblade high-speed axial fans. [ABSTRACT FROM AUTHOR]

This article proposes a variational Bayesian (VB) inference based on the nonsynchronous array measurement (NAM) (VB-NAM) method in order to obtain the fast and robust localization of low-frequency acoustic sources. To enlarge the aperture size compared with the prototype array, the NAM is performed to measure the acoustic pressures with low-frequency based on the forward power propagation model. The implementation of the NAM can be reformulated into a cross-spectral matrix (CSM) completion problem. Then, to solve the inverse problem of the NAM power propagation model, the VB inference based on the Student-t priors and Kullback–Leibler (KL) divergence optimization is proposed. The advantages of the proposed VB-NAM benefit from the optimization of matrix inversion and adaptive estimation of regularization parameters. The contribution of the adaptive parameter evaluation is to reduce the impact of multiple interferences (such as additive noise and the matrix completion error) in NAM. Finally, both simulations at 800 Hz and experimental results at 1000 Hz are presented to show the validation of the proposed VB-NAM method, even under the anisotropic Gaussian noise conditions. Algorithm performance and iteration process are analyzed to demonstrate the efficiency and robustness. [ABSTRACT FROM AUTHOR]

It is valuable to improve the resolution and speed of acoustic beamforming in real-time measurement, especially fault location, noise mapping, and so on. However, multiplication of the power transfer matrix takes much time in the iteration of the deconvolution algorithm. This article proposes a novel method based on the convolution approximation and GPU platform to obtain acoustic imaging with a high resolution quickly. First, the power propagation matrix is approximated by a symmetric Toeplitz block Toeplitz (STBT) matrix, which can transform the product of matrix and vector into the convolution of two smaller patterns. Three different convolution kernels are derived for the convolution operation, including 2-D variant convolution kernel, 2-D invariant convolution kernel, and 1-D separable invariant convolution kernel. Then, the regularization deconvolution algorithm with the convolution kernel is derived. Besides, the relation between approximation error, time cost, and setting parameters is discussed. The results show that the separable invariant convolution kernel can obtain the fastest imaging and relatively accurate localization, while the time consumption of the variant kernel is the highest. Simulations and experiments are operated on the CPU and GPU to make a comparison, which validates that our proposed deconvolution-GPU implementation can significantly improve the computation speed for the matrix on a large scale. [ABSTRACT FROM AUTHOR]

Abstract: Acoustic imaging is a standard technique for mapping acoustic source powers and positions from limited observations on microphone sensors, which often causes an ill-conditioned inverse problem. In this article, we firstly improve the forward model of acoustic power propagation by considering background noises at the sensor array, and the propagation uncertainty caused by wind tunnel effects. We then propose a robust super-resolution approach via sparsity constraint for acoustic imaging in strong background noises. The sparsity parameter is adaptively derived from the sparse distribution of source powers. The proposed approach can jointly reconstruct source powers and positions, as well as the background noise power. Our approach is compared with the conventional beamforming, deconvolution and sparse regularization methods by simulated, wind tunnel data and hybrid data respectively. It is feasible to apply the proposed approach for effectively mapping monopole sources in wind tunnel tests. [Copyright &y& Elsevier]

Abstract: Near-field aeroacoustic imaging has been the focus of great attentions of researchers and engineers in aeroacoustic source localization and power estimation for decades. Recently the deconvolution and regularization methods have greatly improved spatial resolution of the beamforming methods. But neither are they robust to background noises in the low Signal-to-Noise Ratio (SNR) situation, nor do they provide a wide dynamic range of power estimation. In this paper, we first propose an improved forward model of aeroacoustic power propagation, in which, we consider background noises and forward model uncertainty for the robustness. To solve the inverse problem, we then propose a robust Bayesian super-resolution approach via sparsity enforcing a priori. The sparse prior of source powers can be modeled by double exponential distribution, which can improve the spatial resolution and promote wide dynamic range of source powers. Both the hyperparameters and source powers can be alternatively estimated by the Bayesian inference approach based on the joint Maximum A Priori optimization. Finally our Bayesian approach is compared with some of the state-of-the-art methods on simulated, real and hybrid data. The main advantages of our approach are of robustness to noise, a wide dynamic range, super spatial resolution, and non-necessity for prior knowledge of the source number or SNR. It is feasible to apply it for aeroacoustic imaging with the 2D non-uniform microphone array in wind tunnel tests, especially for near-field monopole and extended source imaging. [Copyright &y& Elsevier]

• Proposed methods localize rotating monopoles at high rotation speed and low SNR. • The rotation blur and Doppler effect is removed by the de-Doppler Beamforming. • The ill-posed inverse problem is solved with high resolution. • Aerodynamic sound sources can be localized in the industrial fan experiment. The rotating-acoustic source localization has played an important role in rotary machine fault diagnosis, engine noise reduction and propeller recognition etc. To achieve high-resolution localization of fast rotating sources, this paper proposes an improved power propagation forward model of rotating acoustic source based on the equivalent source hypothesis. Then the ill-posed inverse problem derived from the forward power model is solved robustly by proposed two sparsity-based methods: the Least Absolute Shrinkage and Select Operator for Rotating-acoustic Sources (LASSO-RS) and Least Angle Regression for Rotating-acoustic Sources (LAR-RS). The contributions of this paper are that, at one hand, the improved forward model is indeed time-invariant for the fast-rotating source owing to the time-domain de-Doppler technique; at the other hand, high-resolution acoustic maps can be obtained by proposed LASSO-RS and LAR-RS thanks to the sparsity-based regularization, even under very low signal-to-noise ratio (SNR). Moreover, the proposed LAR-RS is the data-driven method, rather than the parameter-dependent method such as the LASSO-RS with careful selection of regularization parameter. In simulations and experiments, the proposed methods can robustly localize rotating monopoles in the condition of 3000 RPM rotation speed (50 Hz), −5 dB SNR, and 2500 Hz working frequency. [ABSTRACT FROM AUTHOR]

Beamforming technology is an essential method in acoustic imaging or reconstruction, which has been widely used in sound source localization and noise reduction. The beamforming algorithm can be described as all microphones in a plane simultaneously recording the source signal. The source position is then localized by maximizing the result of the beamformer. Evidence has shown that the accuracy of the sound source localization in a 2D plane can be improved by the non-synchronous measurements of moving the microphone array. In this paper, non-synchronous measurements are applied to 3D beamforming, in which the measurement array envelops the 3D sound source space to improve the resolution of the 3D space. The entire radiated object is covered better by a virtualized large or high-density microphone array, and the range of beamforming frequency is also expanded. The 3D imaging results are achieved in different ways: the conventional beamforming with a planar array, the non-synchronous measurements with orthogonal moving arrays, and the non-synchronous measurements with non-orthogonal moving arrays. The imaging results of the non-synchronous measurements are compared with the synchronous measurements and analyzed in detail. The number of microphones required for measurement is reduced compared with the synchronous measurement. The non-synchronous measurements with non-orthogonal moving arrays also have a good resolution in 3D source localization. The proposed approach is validated with a simulation and experiment. [ABSTRACT FROM AUTHOR]