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    欧进萍

    • 教授     博士生导师   硕士生导师
    • 性别:男
    • 毕业院校:哈尔滨建筑大学
    • 学位:博士
    • 所在单位:建设工程学院
    • 电子邮箱:ojinping@dlut.edu.cn

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    Wireless collection and data fusion method of strain signal in civil engineering structures

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    论文类型:期刊论文

    发表时间:2009-01-01

    发表刊物:SENSOR REVIEW

    收录刊物:SCIE、EI、Scopus

    卷号:29

    期号:1

    页面范围:63-69

    ISSN号:0260-2288

    关键字:Condition monitoring; Strain measurement; Structures; Sensors

    摘要:Purpose - The purpose of this paper is to describe a wireless strain sensor system which will allow easier collection of accurate strain signals in civil engineering structures. The sensor system is developed by integrating with resistance strain gauge, and the data fusion method is proposed based on batch estimation theory.
       Designtmethodology/approach - The principle of resistance strain gauge is discussed and the project of wireless acquisition system of strain signal is given. Wireless strain sensor is integrated with modularization method. Based on batch estimation theory the data fusion method of strain signal is described. The experiment of wireless strain sensor system is finished on a typical concrete beam structure, the measure data processed by using the data fusion method and the arithmetic average value method is compared and analyzed.
       Findings - The research result shows that the wireless strain sensor can be installed easily and thus is applied compatibly to local monitoring in civil engineering. The strain signal processed by the data fusion method is more accurate than the one processed by the arithmetic average value method, and thus the proposed data fusion method is fit for processing such slowly-changing signals as strain.
       Originality/value - In this paper, the innovation is shown from two views: one is applying wireless technique to collect strain signals; another is that data fusion with wide application can make measurements more precise and reliable by eliminating uncertain value than using the arithmetic average value method. in general, the developed wireless sensor system and the proposed data fusion method are fit for local monitoring.