Investigations into the sensing mechanism of acoustic emission sensors for particle size measurement in a particular case: normal incidence

Zhang, Guoqiang; Yan, Yong; Hu, Yonghui; Zheng, Ge

doi:10.1088/1361-6501/abe338

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…At present, acoustic emission (AE) technology is broadly used in the structural health monitoring of aerospace structures, pressure vessels, pipelines, bridges, and other the industrial structures [2][3][4]. In consequence, AE technique has also shown great potential for the crack detection in the railway system.…”

Section: Introductionmentioning

confidence: 99%

An improved rail crack detection method based on the wavelet subband LMS adaptive filtering and wavelet analysis

Wang,

Li,

Qian

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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Rail health monitoring plays an important role in the recent railway system. In order to implement acoustic emission technique in rail health monitoring, the mechanical noise caused by wheel-rail interactions should be eliminated with digital signal processing techniques in advance. This paper proposed a wavelet subband least mean square (LMS) adaptive filter, which can be used to effectively eliminate the strong noise and detect crack signals in railway. In such a method, the noisy input signals and reference noise should be first transformed into multi-scale wavelet coefficients. Then the decomposed noisy input and reference of the same channel was input into the separate LMS adaptive filters and the parameters of the filters were optimized by the metric of similarity coefficient. Finally, the denoised wavelet components in different levels were integrated into the whole denoised signal. Experimental tests clearly demonstrate that, compared to the conventional LMS method and adaptive wavelet filtering method, the proposed algorithm can improve the detectability, obtain a higher SNR, and at the same time retain the details of the crack signals under the actual railway noise interference.

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Section: Introductionmentioning

confidence: 99%

An improved rail crack detection method based on the wavelet subband LMS adaptive filtering and wavelet analysis

Wang,

Li,

Qian

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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“…Acoustic emission (AE) technique is a promising structural health monitoring method, which has been broadly applied in the detection of fatigue cracks in the engineering structures [8], measurements of the pulverized particle size [9] and leakage localization in the gas pipeline [10]. Owing to its high sensitivity to the measurements of micro deformation and micro dislocation, AE technique has shown great potential in rail health monitoring and has received considerable attention [11,12].…”

Section: Introductionmentioning

confidence: 99%

Feasibility investigation of rail monitoring by wheel-mounted acoustic emission technology using two-dimensional analytical contact acoustic nonlinearity model

Wang¹,

Zhang²,

Song³

et al. 2021

Meas. Sci. Technol.

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In an attempt to analyze the feasibility of a rail monitoring scheme with a wheel-mounted acoustic emission measurement method, a two-dimensional analytical spring model was proposed to interpret the interactions between acoustic waves and stationary wheel-rail contact interfaces. The spring model represents the coupling strength of the interfaces with their stiffness. It accommodates the environmental information of contact stiffness and the acoustic source information, such as the feature frequency and the incident angle in the analysis of interface transmissibility. The fractal dimension of the interface is introduced into the spring model to interpret the effect of the axle load on the interface stiffness and the variation in transmissibility. Discrete Rayleigh integration is further combined to obtain the directivity of the interface and estimate the acoustic field intensity in the entire wheel. A wheel-rail contact rig was designed to simulate the actual contact conditions in a railway. Experimental data acquired from this test rig were utilized to validate the model, with regard to the interfacial stiffness in normal incidence and amplitudes of the transmitted waves in oblique incidence. After verifying the reliability of the model, the acoustic field intensity in the wheel was visualized under the assumed environmental conditions. Finally, a discussion is presented to determine a proper angular separation for the wheel-mounted sensor system, and substantiate the feasibility of the system.

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Investigations into the sensing mechanism of acoustic emission sensors for particle size measurement in a particular case: normal incidence

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 2 publications

References 30 publications

An improved rail crack detection method based on the wavelet subband LMS adaptive filtering and wavelet analysis

An improved rail crack detection method based on the wavelet subband LMS adaptive filtering and wavelet analysis

Feasibility investigation of rail monitoring by wheel-mounted acoustic emission technology using two-dimensional analytical contact acoustic nonlinearity model

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