Title: Bolt quality testing research using weighted fusion algorithm based on correlation function

Authors: Xiaoyun Sun; Hui Xing; Guang Han; Jiulong Cheng; Yongbang Yuan; Jianpeng Bian; Haiqing Zheng; Mingming Wang

Addresses: School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China ' School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China ' School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China ' State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology, Beijing 100083, China ' State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology, Beijing 100083, China ' School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China ' School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China ' School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

Abstract: Bolt length is an important factor for quality evaluation of anchors. Because of the harsh detection environment and the interference caused by instruments, bolt testing signal contains a lot of noises that make it difficult to analyse and predict the parameters of anchor bolts accurately. In this paper, a non-destructive method based on information fusion of pseudo random signals is presented for bolt quality testing. After pseudo-random signals are generated by multiple sources, weighted fusion algorithm based on correlation function is proposed for fusion processing. Compared with the D-S fusion algorithm and average weighted fusion approach, the correlation fusion is verified to have higher accuracy and better retention of frequency characteristic. Finally, this proposed approach is proved to be more suitable for random signal fusion.

Keywords: anchor bolt modelling; non-destructive testing; D-S fusion algorithm; weighted fusion based on correlation function.

DOI: 10.1504/IJMIC.2018.093541

International Journal of Modelling, Identification and Control, 2018 Vol.30 No.1, pp.19 - 29

Received: 08 Mar 2017
Accepted: 14 Aug 2017
Published online: 27 Jul 2018 *

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