Parameter Optimisation of Wavelet Denoising for Pulsed Eddy Current Signals Based on Particle Swarm Optimisation Algorithm

Abstract

To ensure the reliable operation of high voltage cables, it is crucial to routinely inspect the lead sealing using pulsed eddy current. But nevertheless, the signals can be impacted by noise in actual engineering. While retaining the useful high-frequency information in the original signal, wavelet denoising can filter the clutter efficiently. However, previous research on wavelet denoising has only used it as a straightforward filtering tool, ignoring the impact of changing the parameters on the actual denoising effect. In this study, a more accurate evaluation index called P NCC is constructed for pulsed eddy current testing signals with a focus on maintaining the peak information. This index is then adopted as a fitness function for the particle swarm optimisation algorithm to determine the befitting wavelet denoising parameters and denoised signals. The results show that the signal-to-noise ratio of the signals after denoising is approximately 25 dB, and the distortion at the peak position is stable at about 1%. It demonstrates that the comprehensive index P NCC is a reliable metric for assessing the quality of pulsed eddy current signals. The optimal wavelet denoising parameters for pulsed eddy current signals are found to be the Sym 4 wavelet, 10-layer decomposition and Median threshold function.