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计算机工程 ›› 2011, Vol. 37 ›› Issue (6): 234-236. doi: 10.3969/j.issn.1000-3428.2011.06.081

• 工程应用技术与实现 • 上一篇    下一篇

一种新型的桥梁健康检测算法

李 静,杨小帆,孙启干   

  1. (重庆大学计算机学院,重庆400044)
  • 出版日期:2011-03-20 发布日期:2011-03-29
  • 作者简介:李 静(1986-),女,硕士研究生,主研方向:模式识别,并行计算;杨小帆,教授、博士;孙启干,硕士研究生
  • 基金资助:
    教育部新世纪优秀人才支持计划基金资助项目(NCET-05- 0759);重庆市自然科学基金资助重点项目(CSTC2008BB2195)

New Bridge Health Detection Algorithm

LI Jing, YANG Xiao-fan, SUN Qi-gan   

  1. (College of Computer Science, Chongqing University, Chongqing 400044, China)
  • Online:2011-03-20 Published:2011-03-29

摘要: 针对大型桥梁故障诊断问题,提出一种新型高效的诊断算法。该算法将一座桥梁系统看成由大量的“团”组成,每个“团”由一个挠度检测点及其相应的传感器构成,具体分3个阶段进行:(1)受到“挠度共振”的启发,根据线性回归预测理论建立一个测试模型;(2)利用测试模型对所有“团”进行测试;(3)在测试基础上进行综合分析得出检测结果。对真实桥梁进行检测实验,结果表明该方法故障检测率达81.8%。

关键词: 桥梁系统, 健康检测, 挠度共振, 线性回归, 异常“团”

Abstract: In order to solve the problem of large-scale bridge fault diagnosis, this paper proposes a new efficient algorithm. It addresses the issue of identifying abnormal cliques on bridges, where each clique is composed of a deflection checkpoint and the dedicated sensor. The detection method consists of three stages. First, inspired by “deflection resonance” between different checkpoints, a test pattern is created based on the theory of regression. Second, a set of tests on cliques are conducted based on the established test pattern, and the test outcomes are collected to form a syndrome. Finally, a detection result is achieved based on the syndrome. The proposed method is then applied to the detection of a real-life bridge, and result shows the detection rate reaches 81.8%.

Key words: bridge system, health detection, deflection resonance, linear regression, abnormal clique

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