Adaptive fast Walsh-Hadamard transform for magnetic flux leakage signal of broken wire damage extraction under noise background

Abstract

ABSTRACT Steel wire rope usually works in harsh environments, making it prone to damage during frequent use. Magnetic flux leakage testing is an important way of non-destructive testing, preventing some major accidents of hoist equipment by identifying the damage to steel wire ropes. Whereas, the harsh environment usually generates noise and results in difficulty in extracting the damage features of the magnetic flux leakage signal, which makes it hard to identify the damage. The fast Walsh-Hadamard transform is widely used in signal processing. The denoising effect depends closely on the transform coefficients. If the transformation coefficients can be adaptively retained, better denoising effects can be achieved. Therefore, we propose the adaptive fast Walsh-Hadamard transform to extract the magnetic flux leakage signal of steel wire rope under noise background. Using the cross-correlation co efficient and the similarity of the greyscale histogram as the indexes, the Fast Walsh-Hadamard transform combines the adaptive particle swarm optimisation to adaptively retain the optimal transformation coefficient and then gets the optimal denoising signal. Meanwhile, compared to wavelet denoising methods, the adaptive fast Walsh-Hadamard transform can effectively improve denoising performance in peak-to-peak reductivity and can be successfully used in steel wire rope damage extraction.