X-ray testing and engineering diagnostics

Зуев, В. М.; Капустин, В. И.; Karpenko, A. I.; Van’kova, N. E.

doi:10.1134/s1061830906030107

Cited by 1 publication

(2 citation statements)

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“…In the LSSVM algorithm, the kernel function is the radial basis function shown in Equation (10). The value ranges of the regularization parameter γ and the kernel function parameter σ 2 are set to be [0, 300] and [0, 200], respectively.…”

Section: Other Methodsmentioning

confidence: 99%

“…Traditional nondestructive testing techniques have some limitations [4][5][6][7]. For example, magnetic particle testing can only detect ferromagnetic materials [8] and requires a low environment temperature; eddy current testing [9] can only detect conductive metal materials and non-metallic materials that can generate eddy currents; and radiographic testing [10] has high detection costs and slow detection speeds. Compared with these techniques, ultrasonic-guided wave detection has the advantages of small energy attenuation, an extensive detection range, and a fast detection speed.…”

Section: Introductionmentioning

confidence: 99%

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A New Method for Erosion Prediction of 90° Elbow Based on Non-Axisymmetric Ultrasonic-Guided Wave and the PSO–LSSVM Algorithm

Wang

Zhou

et al. 2023

Sensors

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The non-axisymmetric exciting guided wave can detect the thinning section of the elbow, and the time domain energy value of the signal collected at the outer arch position of the receiving end displays a downward trend as the remaining thickness of the erosion area decreases. To address the difficulty in detecting the erosion degree of the elbow with high accuracy, this paper uses the linear frequency modulation (LFM) signal to excite a non-axisymmetric guided wave that propagates in the 90° elbow and collects signals through four PZT receivers. To predict the erosion degree, the corresponding relationship between the energy value of the four signals after fractional Fourier filtering and the degree of elbow erosion is established through the particle swarm optimization (PSO)–least squares support vector machine (LSSVM) algorithm. The results show that the method proposed has an average accuracy rate of 98.1864%, 94.7167%, 99.119%, and 99.9593% for predicting the erosion degree of four elbow samples, and 94.0039%. and 81.2976% for two new erosion degrees, which are higher than the nonlinear regression model, LSSVM algorithm, and BP neural network algorithm. This study has guiding significance for real-time monitoring of elbow erosion.

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Section: Other Methodsmentioning

confidence: 99%