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  • Open Access


    Acoustic Emission Recognition Based on a Three-Streams Neural Network with Attention

    Kang Xiaofeng1, Hu Kun2,*, Ran Li3

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2963-2974, 2023, DOI:10.32604/csse.2023.025908

    Abstract Acoustic emission (AE) is a nondestructive real-time monitoring technology, which has been proven to be a valid way of monitoring dynamic damage to materials. The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning. Considering that the huge success of deep learning technologies, where the Recurrent Neural Network (RNN) has been widely applied to sequential classification tasks and Convolutional Neural Network (CNN) has been widely applied to image recognition tasks. A novel three-streams neural network (TSANN) model is proposed in this paper to deal with fault detection tasks. Based on residual connection… More >

  • Open Access


    Evaluation of Self-Healing Efficiency of Microcapsule-Based Self-Healing Cementitious Composites Based on Acoustic Emission

    Wenfeng Hao1,*, Hao Hao2, Humaira Kanwal2, Shiping Jiang2,*

    Journal of Renewable Materials, Vol.11, No.4, pp. 1687-1697, 2023, DOI:10.32604/jrm.2022.023795

    Abstract Microcapsule self-healing technology is one of the effective methods to solve the durability problem of cement-based composites. The evaluation method of the self-healing efficiency of microcapsule self-healing cement-based composites is one of the difficulties that limits the self-healing technology. This paper attempts to characterize the self-healing efficiency of microcapsule self-healing cement-based composites by acoustic emission (AE) parameters, which provides a reference for the evaluation of microcapsule self-healing technology. Firstly, a kind of self-healing microcapsules were prepared, and the microcapsules were added into the cement-based composites to prepare the compression samples. Then, the specimen with certain pre damage was obtained by… More >

  • Open Access


    Efficient Authentication System Using Wavelet Embeddings of Otoacoustic Emission Signals

    V. Harshini1, T. Dhanwin1, A. Shahina1,*, N. Safiyyah2, A. Nayeemulla Khan2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1851-1867, 2023, DOI:10.32604/csse.2023.028136

    Abstract Biometrics, which has become integrated with our daily lives, could fall prey to falsification attacks, leading to security concerns. In our paper, we use Transient Evoked Otoacoustic Emissions (TEOAE) that are generated by the human cochlea in response to an external sound stimulus, as a biometric modality. TEOAE are robust to falsification attacks, as the uniqueness of an individual’s inner ear cannot be impersonated. In this study, we use both the raw 1D TEOAE signals, as well as the 2D time-frequency representation of the signal using Continuous Wavelet Transform (CWT). We use 1D and 2D Convolutional Neural Networks (CNN) for… More >

  • Open Access


    Study on Acoustic Emission Characteristics of Self-Compacting Concrete under Uniaxial Compression Test

    Yongshuai Sun1,*, Guihe Wang2, Yixuan li2

    Journal of Renewable Materials, Vol.10, No.8, pp. 2287-2302, 2022, DOI:10.32604/jrm.2022.019660

    Abstract To study the relationship between acoustic emission characteristic parameters of self-compacting concrete(SCC) and its destruction evolution, under uniaxial compression, acoustic emission(AE) tests are performed on C30 selfcompacting concrete test blocks that are preserved for 7 days and 28 days, the corresponding relationship among energy, amplitude, ring count and different failure stages of the specimens are analyzed by AE experiment, and the spatial distribution of AE in each stage is described by introducing location map. The test shows that there are two rules for the failure of SCC specimens cured for 7 days and 28 days: (1) The first failure law… More >

  • Open Access


    Fusion Fault Diagnosis Approach to Rolling Bearing with Vibrational and Acoustic Emission Signals

    Junyu Chen1, Yunwen Feng1,*, Cheng Lu1,2, Chengwei Fei2

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 1013-1027, 2021, DOI:10.32604/cmes.2021.016980

    Abstract As the key component in aeroengine rotor systems, the health status of rolling bearings directly influences the reliability and safety of aeroengine rotor systems. In order to monitor rolling bearing conditions, a fusion fault diagnosis method, namely empirical mode decomposition (EMD)-Mahalanobis distance (E2MD) and improved wavelet threshold (IWT) (E2MD-IWT) for vibrational signals and acoustic emission (AE) signals is developed to improve the diagnostic accuracy of rolling bearings. The IWT method is proposed with a hard wavelet threshold and a soft wavelet threshold. Moreover, it is shown to be effective through numerical simulation. EMD is utilized to process the original AE… More >

  • Open Access


    Acoustic Emission Characteristics of Different Bamboo and Wood Materials in Bending Failure Process

    Ting Wang1, Zhiqiang Wang1,*, Yin Yang1, Jianhui Zhou2,*

    Journal of Renewable Materials, Vol.10, No.2, pp. 527-540, 2022, DOI:10.32604/jrm.2022.017955

    Abstract The acoustic emission (AE) technique can perform non-destructive monitoring of the internal damage development of bamboo and wood materials. In this experiment, the mechanical properties of different bamboo and wood (bamboo scrimber, bamboo plywood and SPF (Spruce-pine-fir) dimension lumber) during four-point loading tests were compared. The AE activities caused by loadings were investigated through the single parameter analysis and K-means cluster analysis. Results showed that the bending strength of bamboo scrimber was 3.6 times that of bamboo plywood and 2.7 times that of SPF dimension lumber, respectively. Due to the high strength and toughness of bamboo, the AE signals of… More > Graphic Abstract

    Acoustic Emission Characteristics of Different Bamboo and Wood Materials in Bending Failure Process

  • Open Access


    Acoustic Shock Emission in a Collision of a Drop with Water Surface

    V. E. Prokhorov*

    FDMP-Fluid Dynamics & Materials Processing, Vol.16, No.4, pp. 737-746, 2020, DOI:10.32604/fdmp.2020.08988

    Abstract The collision of droplets with a water surface is being actively developed in the interests of many applied problems—transfer of matter through the ocean-atmosphere boundary, underwater acoustic noise of the marine environment, measurement of precipitation intensity, various technologies, and much more. One of the research priorities is acoustic radiation, in particular, shock sound arising at the moment a drop contacts the surface. The impact of the drop is preceded by processes that affect the shape of the drop, because of which it noticeably deviates from the spherical one. As a result, the final (contact) velocity changes–one of the most important… More >

  • Open Access


    Acoustic Emission Recognition Based on a Two-Streams Convolutional Neural Network

    Weibo Yang1, Weidong Liu2, *, Jinming Liu3, Mingyang Zhang4

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 515-525, 2020, DOI:10.32604/cmc.2020.09801

    Abstract The Convolutional Neural Network (CNN) is a widely used deep neural network. Compared with the shallow neural network, the CNN network has better performance and faster computing in some image recognition tasks. It can effectively avoid the problem that network training falls into local extremes. At present, CNN has been applied in many different fields, including fault diagnosis, and it has improved the level and efficiency of fault diagnosis. In this paper, a two-streams convolutional neural network (TCNN) model is proposed. Based on the short-time Fourier transform (STFT) spectral and Mel Frequency Cepstrum Coefficient (MFCC) input characteristics of two-streams acoustic… More >

  • Open Access


    A Rub-Impact Recognition Method Based on Improved Convolutional Neural Network

    Weibo Yang1, *, Jing Li2, Wei Peng2, Aidong Deng3

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 283-299, 2020, DOI:10.32604/cmc.2020.07511

    Abstract Based on the theory of modal acoustic emission (AE), when the convolutional neural network (CNN) is used to identify rotor rub-impact faults, the training data has a small sample size, and the AE sound segment belongs to a single channel signal with less pixel-level information and strong local correlation. Due to the convolutional pooling operations of CNN, coarse-grained and edge information are lost, and the top-level information dimension in CNN network is low, which can easily lead to overfitting. To solve the above problems, we first propose the use of sound spectrograms and their differential features to construct multi-channel image… More >

  • Open Access


    Coal Rock Condition Detection Model Using Acoustic Emission and Light Gradient Boosting Machine

    Jing Li1, Yong Yang2, *, Hongmei Ge1, Li Zhao3, Ruxue Guo3, 4

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 151-162, 2020, DOI:10.32604/cmc.2020.05649

    Abstract Coal rock mass instability fracture may result in serious hazards to underground coal mining. Acoustic emissions (AE) stimulated by internal structure fracture should carry lots of favorable information about health condition of rock mass. AE as a sensitive non-destructive test method is gradually utilized to detect anomaly conditions of coal rock. This paper proposes an improved multi-resolution feature to extract AE waveform at different frequency resolutions using Coilflet Wavelet Transform method (CWT). It is further adopt an efficient Light Gradient Boosting Machine (LightGBM) by several cascaded sub weak classifier models to merge AE features at different views of frequency for… More >

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