Study of a Finite-Element-Based Spatial Distribution Model for Coal-Mining Wire-Rope Detection Signal


如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅存取

详细

Owing to inadequacies in the current technology for coal-mining wire-rope detection, a spatial distribution model for the coal-mining wire-rope detection signal based on a finite element analysis (FEA) is proposed. First, the three-dimensional FEA model for the coal-mining wire-rope-defect detection signal was developed to analyse the spatial characteristics of the magnetic flux leakage (MFL) signal of the coal-mining wire rope. On the basis of the results, the FEA model was experimentally validated. The experimental and FEA model results exhibited consistent patterns of change, and the research methods used in this study were demonstrated to be feasible. Lastly, the experimental prototype equipment was tested through simulation and experiments. The results show that compared with the conventional defect detection equipment, the new-type equipment detected 7 defects accurately in the experiment while the conventional equipment missed two. The signal-to-noise ratio of the new equipment is 33.1 dB higher than that of the conventional one, which reveals that it presents a better detection effect.

作者简介

Hongyao Wang

School of Mechanical Electronic and Information Engineering

Email: hongyaowang2004@163.com
中国, Beijing

Jie Tian

School of Mechanical Electronic and Information Engineering

编辑信件的主要联系方式.
Email: hongyaowang2004@163.com
中国, Beijing

Guoying Meng

School of Mechanical Electronic and Information Engineering

Email: hongyaowang2004@163.com
中国, Beijing

Xiaowei Li

School of Mechanical Electronic and Information Engineering

Email: hongyaowang2004@163.com
中国, Beijing

Junying Zhou

School of Mechanical Electronic and Information Engineering

Email: hongyaowang2004@163.com
中国, Beijing

Xin Lü

School of Mechanical Electronic and Information Engineering

Email: hongyaowang2004@163.com
中国, Beijing


版权所有 © Pleiades Publishing, Ltd., 2019
##common.cookie##