Wire Rope Damage Detection Based on Magnetic Leakage and Visible Light

Chen, Quankun; Zhang, Juwei; Q, Ye

doi:10.1007/s11668-023-01682-z

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Wavelet threshold noise reduction selectively suppresses irrelevant signal components and enhances the useful ones [5][6]. The denoising process consists of the following specific steps: (2) Thresholding process: To acquire estimated wavelet coefficients, wavelet thresholding is performed on the decomposition coefficients of all the layers.…”

Section: Wavelet Thresholding Algorithmmentioning

confidence: 99%

Application of EMD combined with wavelet algorithm for filtering slag noise in steel cord conveyor belt

Sun,

Zhang,

Liu

et al. 2023

J. Phys.: Conf. Ser.

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This study proposes a noise reduction technique that combines empirical mode decomposition (EMD) with wavelet thresholding to remove noise interference generated by metal slag in the damage signal of steel cord conveyor belts. The initial step involves decomposing the original signal into high-frequency and low-frequency intrinsic mode function (IMF) components using empirical mode decomposition (EMD), guided by the metal slag signal characteristics analysis. The high-frequency noise is filtered away while the low-frequency IMF components are kept intact through the wavelet threshold de-noising technique. The slag noise-containing IMF components are deleted, and the filtered signal and the low-frequency IMF components are utilized for signal reconstruction to provide the optimum noise reduction result. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

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Section: Wavelet Thresholding Algorithmmentioning

confidence: 99%