桥梁预警系统的数据预处理

上海交通大学学报（自然版） ›› 2012, Vol. 46 ›› Issue (10): 1680-1686.

桥梁预警系统的数据预处理

陈明

(上海应用技术学院轨道交通学院，上海 201418)

收稿日期:2012-06-10 出版日期:2012-10-30 发布日期:2012-10-30
基金资助:
上海市科学技术委员会科研计划项目（10230501400），铁道部科技研究开发计划项目（2012G003E）

Data Preprocess for Bridge Damage Alarming System

CHEN Ming

(School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China)

Received:2012-06-10 Online:2012-10-30 Published:2012-10-30

摘要/Abstract

摘要： 针对影响桥梁预警系统有效工作的数据获取问题，提出了融合神经规则、数据分类和数据评估的预警系统数据预处理方法.通过训练集将人工完成数据处理过程中所用到的知识融入到神经规则中，并应用神经规则剔除噪声数据；根据拟定的相似性指标对神经规则输出的数据进行分类，合并相似度较大的信息，以大幅度降低数据量；应用曲率模态对各测点保留的信息进行剖分，并与桥梁结构各测点的标准曲率模态比较，提取与初始数据信息不一致的采样数据，为预警系统的损伤识别提供依据.模拟分析表明，该方法能够在大幅度降低预警系统数据量的基础上保留结构状态发生变化的关键信息，具有一定的应用价值.

关键词: 桥梁预警系统, 数据处理, 预处理

Abstract: Bridge alarming system data sets are often interspersed with noise. If it does not consider noise, the data process algorithm in alarming system may not provide accurate results. A new hybrid approach comprising of neural rules, data classification and data evaluation was proposed to carry out data selection. Specifically, the neural rules remove noise data by applying the principles of an intelligent reasoning process. Data classification classifies the patterns in the reduced datasets based on the similarity index. The data evaluation applies curvature model to extract kernel information of bridge structure. The simulation results demonstrate that the proposed approach produces good classification accuracy and a higher level of consistency to bridge damage condition.

Key words: bridge alarming system, data process, preprocess

中图分类号:

U 44

陈明. 桥梁预警系统的数据预处理 [J]. 上海交通大学学报（自然版）, 2012, 46(10): 1680-1686.

CHEN Ming. Data Preprocess for Bridge Damage Alarming System[J]. Journal of Shanghai Jiaotong University, 2012, 46(10): 1680-1686.

参考文献

［1］Majumder L, Manohar C S. A timedomain approach for damage detection in beam structures using vibration data with a moving oscillator as an excitation source［J］. Journal of Sound and Vibration, 2003, 268(4): 699716.
［2］Rucka M, Wilde K. Application of continuous wavelet transform in vibration based damage detection method for beams and plates［J］. Journal of Sound and Vibration, 2006, 297( 35):536550.
［3］He Rongsong, Hwang Shunfa. Damage detection by an adaptive realparameter simulated annealing genetic algorithm［J］. Computers & Structures, 2006, 84(3132): 22312243.
［4］Yeung W T, Smith J W. Damage detection in bridges using neural networks for pattern recognition of vibration signatures［J］. Engineering Structures, 2005, 27(5):685698.
［5］荆龙江,项贻强. 基于柔度矩阵法的大跨斜拉桥主梁的损伤识别［J］.浙江大学学报：工学版

, 2008, 42(1):164169.
JING Longjiang, XIANG Yiqiang, Damage identification of main girders for long span cablestayed bridge based on flexibility matrix［J］. Journal of Zhejiang University： Engineering Science, 2008, 42(1):164169.
［6］陈晓强,朱宏平,閤东东. 基于伪比能的梁式结构损伤识别［J］. 振动与冲击, 2008, 27(2):2933.
CHEN Xiaoqiang, ZHU Hongping, GE Dongdong. Damage identification of beam structures based on pseudo strain energy density［J］. Journal of Vibration and Shock, 2008, 27(2): 2933.
［7］丁幼亮，李爱群，缪长青. 基于小波包能量谱的结构损伤预警方法研究［J］. 工程力学, 2006, 23(8): 4248.
DING Youliang, LI Aiqun, MIAO Changqing, Investigation on the structural damage alarming method based on wavelet packet energy spectrum［J］. Engineering Mechanics, 2006, 23(8): 4248.
［8］Pandey A K, Biswas M, Samman M M. Damage detection from changes in curvature mode shapes［J］. Journal of Sound and Vibration, 1991,145(2): 32132.
［9］Yang Y B, Chen Y J. A new direct method for updating structural models based on measured modal data［J］. Engineering Structures, 2009, 31(1):3242.

[1]	余昊a，孙锬锋a,b，蒋兴浩a,b. 基于光流块统计特征的视频异常行为检测算法[J]. 上海交通大学学报（自然版）, 2015, 49(08): 1199-1204.
[2]	成谢锋,傅女婷. 心音身份识别综述[J]. 上海交通大学学报（自然版）, 2014, 48(12): 1745-1750.
[3]	崔霞1, 施光林2, 沈伟3. 基于分组数据处理神经网络气动人工肌肉迟滞特性[J]. 上海交通大学学报（自然版）, 2012, 46(06): 931-935.