Title: Identification of mining steel rope broken wires based on improved EEMD

Authors: Tie-Zhu Qiao; Zhao-Xing Li; Bao-Quan Jin

Addresses: Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China ' Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China ' Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China

Abstract: Metal magnetic memory (3M), which is used to evaluate the stress concentration and fatigue damage of ferromagnetic materials, belongs to a weak magnetic detection. When 3M technology is used to detect working steel rope, the detected signal contains lots of interfering signals to distort real signal. To improve signal to noise ratio (SNR), ensemble empirical mode decomposition (EEMD) is used to denoise the original signal. A new method of confirming white noise standard deviation (WNSD) added to EEMD has been proposed, which has a higher accuracy. Besides, a self-adaptive method of selecting intrinsic mode functions (IMFs) has been proposed. The method is used to denoise magnetic memory signal of steel rope metal and the result is better than other common denoising methods. After denoising the tested signal of steel rope, the broken wires field of the rope can be recognised by the normal component of 3M crossing zero point.

Keywords: broken wires; EEMD denoising; ensemble empirical mode decomposition; 3M signals; metal magnetic memory; stress concentration; fatigue damage; ferromagnetic materials; SNR; signal to noise ratio; mining steel ropes; white noise standard deviation; mining industry.

DOI: 10.1504/IJMME.2016.078359

International Journal of Mining and Mineral Engineering, 2016 Vol.7 No.3, pp.224 - 236

Received: 19 Aug 2015
Accepted: 01 Mar 2016

Published online: 02 Aug 2016 *

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