Journal of South China University of Technology (Natural Science Edition) ›› 2019, Vol. 47 ›› Issue (3): 70-77.doi: 10.12141/j.issn.1000-565X.180383

• Mechanical Engineering • Previous Articles     Next Articles

Fusion Assessment on the Credibility of Cloud Nondestructive Testing for Large-Scale Steel Structures

 HONG Xiaobin1 ZI Wenjiang1 YU Rong1 LUO Zongqiang1 HE Zhenwei 2   

  1.  1． School of Mechanical and Automotive Engineering，South China University of Technology，Guangzhou 510640， Guangdong，China; 2． Shenzhen CIMC Intelligent Technology Co． ，Ltd． ，Shenzhen 518035，Guangdong，China
  • Received:2018-07-26 Revised:2018-11-22 Online:2019-03-25 Published:2019-01-31
  • Contact: 罗宗强( 1966-) ，男，博士，教授级高工，主要从事高性能金属材料研究与检测、加工研究 E-mail:mezqluo@scut.edu.cn
  • About author:洪晓斌( 1979-) ，男，博士，教授，博士生导师，主要从事无损检测技术与装备、无人智能测控技术及应用研究
  • Supported by:
    Supported by the Major Science and Technology Project of Guangdong Province( 2017B030305001) 

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Abstract: For the problem that single testing method is difficult to achieve comprehensive assessment of the object，the comprehensive testing using a variety of nondestructive testing methods is suggested to be effective，in which the credibility assessment of comprehensive test results is critical． In the investigation，firstly，the cloud nondestructive testing system architecture for large-scale steel structures was designed based on Hadoop，and the information flow of large-scale steel structures cloud nondestructive testing Hadoop architecture was analyzed． Then，the D-S evidence theory joint operator of credibility fusion of large-scale steel structures cloud nondestructive testing data was defined，and the multi-source data credibility MapＲeduce fusion algorithm based on D-S evidence theory was proposed． Finally，a large steel tube tower structure testing experimental platform was established，and the credibility of visual endoscopic inspection，eddy current testing and conventional ultrasonic inspection multisource data was tested by fusion evaluation． The results show that the detection rate of defects in each single testing method is improved by the MapReduce fusion algorithm for the credibility of cloud testing data． It meets the cloud nondestructive testing requirements of large-scale steel structures

Key words: Hadoop, D-S evidence theory, MapReduce, large-scale steel structure

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