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The transformer discharge fault localization is important for the power supply reliability. The audible sound radiated by the transformer can reflect the extensive operation information. Thus, discharge faults can be localized by high spatial resolution acoustic imaging techniques to facilitate the inspection and maintenance of power equipment. In this paper, the grid-moving equivalent source method (GMESM) is proposed to visualize the discharge fault on a transformer, where the equivalent source grids can approach the discharge fault position adaptively. A probability framework is established in GMESM. The noise power can be firstly estimated by the Bayes rule to avoid an additional measurement of the background noise. Then, the expectation maximization algorithm is used to optimize the proposed model. Two experiment cases are used to validate the proposed method. It turns out that the proposed GMESM can visualize the discharge fault on the transformer with high spatial resolution and accuracy. • The transformer discharge fault is localized by audible acoustic array. • GMESM is proposed for high-resolution transformer discharge fault localization. • The background noise power is estimated to avoid an additional measurement. [ABSTRACT FROM AUTHOR]

This paper investigates using an Iterated Bayesian Focusing Algorithm and out-duct array measurements to identify multiple broadband sources placed in a cylindrical duct. The problems of large main lobe and low resolution due to localization of sources using conventional methods are investigated in this paper. To solve these problems, an Iterated Bayesian Focusing (IBF) method is utilized to localize the broadband sources. To evaluate and confirm the efficacy of the method, simulation tests and experimental validation are performed. In our experiments, we used a 56-channel dobby spiral array for out-duct measurements and beamforming method, equivalent source method (ESM), deconvolution approach for the mapping of acoustic sources (DAMAS), and Iterated Bayesian Focusing (IBF) method to localize broadband sources inside the duct. Simulation and experimental results demonstrate that the Iterated Bayesian Focusing (IBF) method significantly reduces the main lobe width and minimizes the number of minor lobes. This leads to improved accuracy in localizing double sources. • Broadband localization of stationary sources in a cylindrical duct is investigated. • Investigated using an out-duct microphone array for localizing induct sound sources. • Localization of a single sound source versus two sources is investigated. • Iterated Bayesian Focusing (IBF) is applied to improve the resolution of the results from the conventional methods. [ABSTRACT FROM AUTHOR]

This paper presents an experimental investigation of rotating source localization to identify the axial fan noise with high spatial resolution methods. It exploits the on time-domain de-Doppler, and sparsity-based inversion and the convolution model to approximate the power propagation model of the rotating source. This work is also an extension of the previous paper of authors. But the focus of this study is the experimental investigation and application of actual data. To evaluate the performance of the proposed algorithm, a vortex generator is attached to the fan blade, creating discontinuities in the blade to simulate axial fan blade defects (cracks, holes, dust accumulations, etc.). The experimental data are shared as a data benchmark to promote further studies in the field of rotating acoustic localization. Compared with the state-of-the-art rotating Beamforming method (ROSI), the acoustic imaging results at various working frequencies for three different axial fans with different rotating speeds (RPM) are accurately identified and sparsely distributed. In addition, the power propagation model derived in the previous paper can be interpreted by a convolutional approximation model in this paper, and this technique can help to reduce the computational costs of proposed methods for real applications. The accuracy of the aerodynamic noise positions estimated by the proposed method are further proved to match well with the aerodynamic mechanism. • A practical implementation for the rotating sound sources localization in industrial axial fans is presented. • Two types of vortex generators (VGs) are attached to the blade, simulating the blade defects. • An explanation for the power propagation model based on the convolutional approximation is presented. • A data benchmark is established for peers to compare various rotating sound source localization methods. [ABSTRACT FROM AUTHOR]

• Cyclic correlation density decomposition based on the sparse and low-rank model is proposed. • Weak fault features of rolling bearings can be extracted with the proposed method. • Simulation and experimental results validate the effectiveness of the proposed method. The weak fault feature extraction of rolling element bearings is of critical interest for fault diagnosis. The initial fault is always very weak and buried in high background noise, making it extremely hard to extract the fault feature. Thus, it is essential to correctly extract the weak fault feature of the rolling bearings. A weak fault feature extraction approach using Cyclic correlation density decomposition based on the sparse and low-rank model is proposed in this article. According to the cyclic statistical properties of the fault-bearing signal, the Fast Spectral Correlation (Fast-SC) algorithm is employed to obtain the Cyclic Spectral Density (CSD). It is founded that the CSD of periodic impulse exhibits a high degree of sparsity. Then, the sparsity is exploited into the sparse and low-rank decomposition model to extract the fault features. The CSD is decomposed into two components with the Robust Principal Component Analysis (RPCA) algorithm. The sparse component corresponds to the periodic fault impulse, while the low-rank component represents interference. It is noteworthy that the decomposed sparse component exhibits high resolution and high sparsity, which means the bearing fault features can be revealed clearly and accurately. Finally, the rolling bearing fault feature is detected effectively by the Enhanced Envelope Spectrum (EES). Both simulation fault signal and experimental data are analyzed to verify the proposed method's performance. A Frequency Component Indicator (FCI) and a Relative Indicator Gain (RIG) are constructed to quantify the comparison of the extraction results. The RIG of the proposed method relative to the envelope spectrum is more than 10 dB in the simulated bearing signal with a low SNR of-10 dB, and more than 6 dB in the experimental case. These results validate that the proposed method can extract weak fault features more effectively than some existing methods. [ABSTRACT FROM AUTHOR]