首页> 外文期刊>Mathematical Problems in Engineering >Novel Damage Detection Techniques for Structural Health Monitoring Using a Hybrid Sensor
【24h】

Novel Damage Detection Techniques for Structural Health Monitoring Using a Hybrid Sensor

机译:使用混合传感器的结构健康监测的新型损伤检测技术

获取原文
获取原文并翻译 | 示例
           

摘要

This study presents a technique for detecting fatigue cracks based on a hybrid sensor monitoring system consisting of a combination of intelligent coating monitoring (ICM) and piezoelectric transducer (PZT) sensors. An experimental procedure using this hybrid sensor system was designed to monitor the cracks generated by fatigue testing in plate structures. A probability of detection (POD) model that quantifies the reliability of damage detection for a specific sensor or the nondestructive testing (NDT) method was used to evaluate the weight factor for the ICM and PZT sensors. To estimate the uncertainty of model parameters in this study, the Bayesian method was employed. Realistic data from fatigue testing was used to validate the overall method, and the results show that the novel damage detection technique using a hybrid sensor can quantify fatigue cracks more accurately than results obtained by conventional sensor methods.
机译:这项研究提出了一种基于混合传感器监控系统的疲劳裂纹检测技术,该系统由智能涂层监控(ICM)和压电换能器(PZT)传感器组合而成。设计了使用该混合传感器系统的实验程序,以监视由板结构疲劳测试产生的裂纹。使用检测概率(POD)模型来量化特定传感器损坏检测的可靠性,或者使用无损检测(NDT)方法来评估ICM和PZT传感器的权重因子。为了估计这项研究中模型参数的不确定性,采用了贝叶斯方法。来自疲劳测试的真实数据用于验证整体方法,结果表明,使用混合传感器的新型损伤检测技术比传统传感器方法获得的结果更准确地量化了疲劳裂纹。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2016年第4期|3734258.1-3734258.13|共13页
  • 作者单位

    Beihang Univ, Sch Reliabil & Syst Engn, 37 Xueyuan Rd, Beijing 100191, Peoples R China;

    Beihang Univ, Sch Reliabil & Syst Engn, 37 Xueyuan Rd, Beijing 100191, Peoples R China;

    Beihang Univ, Sch Reliabil & Syst Engn, 37 Xueyuan Rd, Beijing 100191, Peoples R China;

    Beihang Univ, Sch Reliabil & Syst Engn, 37 Xueyuan Rd, Beijing 100191, Peoples R China;

    Beihang Univ, Sch Reliabil & Syst Engn, 37 Xueyuan Rd, Beijing 100191, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号