Teng Teng^1,2, Yuhe Cai³, Linchao Wang^3,*, Yanzhao Zhu²

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1181-1202, 2024, DOI:10.32604/fdmp.2023.044532 - 27 June 2024

Abstract Liquid nitrogen has shown excellent performances as a good fracturing medium in the extraction of unconventional natural gas, and its application in coalbed methane extraction is currently a research hotspot. This study focuses on the acoustic emission properties of coal specimens treated utilizing liquid nitrogen with varying initial temperatures in a three-point bending environment. Through examination of the load-displacement curves of the considered coal samples, their mechanical properties are also revealed for different initial temperatures and cycling frequencies. The findings demonstrate a gradual decline in the maximum load capacity of coal rock as the temperature… More >

K. KRISHNAMOORTHY^a,*, N. PRABHU^b

Journal of Polymer Materials, Vol.40, No.3-4, pp. 215-226, 2023, DOI:10.32381/JPM.2023.40.3-4.7

Abstract The acoustic emission monitoring with artificial neural networks predicts the ultimate strength of glass/epoxy composite laminates using Acoustic Emission Data. The ultimate loads of all the specimens were used to characterise the emission of hits during failure modes. The six layered glass fiber laminates were prepared (in woven mat form) with epoxy as the binding medium by hand lay-up technique. At room temperature, with a pressure of 30 kg/cm², the laminates were cured. The laminates of standard dimensions as per ASTM D3039 for the tensile test were cut from the lamina. The Acoustic Emission (AE) test More >

Kang Xiaofeng¹, Hu Kun^2,*, Ran Li³

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2963-2974, 2023, DOI:10.32604/csse.2023.025908 - 03 April 2023

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 More >

Wenfeng Hao^1,*, Hao Hao², Humaira Kanwal², Shiping Jiang^2,*

Journal of Renewable Materials, Vol.11, No.4, pp. 1687-1697, 2023, DOI:10.32604/jrm.2022.023795 - 01 December 2022

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… More >

V. Harshini¹, T. Dhanwin¹, A. Shahina^1,*, N. Safiyyah², A. Nayeemulla Khan²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1851-1867, 2023, DOI:10.32604/csse.2023.028136 - 03 November 2022

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… More >

Yongshuai Sun^1,*, Guihe Wang², Yixuan li²

Journal of Renewable Materials, Vol.10, No.8, pp. 2287-2302, 2022, DOI:10.32604/jrm.2022.019660 - 25 April 2022

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:… More >

Ting Wang¹, Zhiqiang Wang^1,*, Yin Yang¹, Jianhui Zhou^2,*

Journal of Renewable Materials, Vol.10, No.2, pp. 527-540, 2022, DOI:10.32604/jrm.2022.017955 - 30 August 2021

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… More > Graphic Abstract

Junyu Chen¹, Yunwen Feng^1,*, Cheng Lu^1,2, Chengwei Fei²

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 1013-1027, 2021, DOI:10.32604/cmes.2021.016980 - 08 October 2021

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… More >

V. E. Prokhorov^*

FDMP-Fluid Dynamics & Materials Processing, Vol.16, No.4, pp. 737-746, 2020, DOI:10.32604/fdmp.2020.08988 - 11 August 2020

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 More >

Weibo Yang¹, Weidong Liu^{2, *}, Jinming Liu³, Mingyang Zhang⁴

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 515-525, 2020, DOI:10.32604/cmc.2020.09801 - 20 May 2020

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) More >