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基于故障预测与健康管理的边坡安全监测预警体系

黄铭,  刘俊

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文章导航 > 工业建筑  > 2020 >  50(5): 66-70,43
黄铭, 刘俊. 基于故障预测与健康管理的边坡安全监测预警体系[J]. 工业建筑, 2020, 50(5): 66-70,43. doi: 10.13204/j.gyjz202005011
引用本文: 黄铭, 刘俊. 基于故障预测与健康管理的边坡安全监测预警体系[J]. 工业建筑, 2020, 50(5): 66-70,43. doi: 10.13204/j.gyjz202005011
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HUANG Ming, LIU Jun. THE SAFETY MONITORING AND PROGNOSTIC SYSTEM OF SLOPE BASED ON PHM[J]. INDUSTRIAL CONSTRUCTION, 2020, 50(5): 66-70,43. doi: 10.13204/j.gyjz202005011
Citation: HUANG Ming, LIU Jun. THE SAFETY MONITORING AND PROGNOSTIC SYSTEM OF SLOPE BASED ON PHM[J]. INDUSTRIAL CONSTRUCTION, 2020, 50(5): 66-70,43. doi: 10.13204/j.gyjz202005011
shu

基于故障预测与健康管理的边坡安全监测预警体系

doi: 10.13204/j.gyjz202005011

  • 1. 合肥工业大学土木与水利工程学院, 合肥 230009;

  • 2. 三峡库区地质灾害教育部重点实验室(三峡大学), 湖北宜昌 443000;

  • 3. 上海交通大学船舶海洋与建筑工程学院, 上海 200240

基金项目: 

水利部公益性行业专项（201401063-02）；安徽省科技攻关计划项目（1604a0802106）；三峡库区地质灾害教育部重点实验室开放研究基金资助项目（2015KDZ03）。

详细信息
    作者简介:

    黄铭,1972年出生,男,教授,博士生导师。电子信箱:Lsxhuangm@hotmail.com

THE SAFETY MONITORING AND PROGNOSTIC SYSTEM OF SLOPE BASED ON PHM

  • 1. School of Civil Engineering, Hefei University of Technology, Hefei 230009, China;

  • 2. Key Laboratory of Geological Hazards on Three Gorges Reservoir Area(China Three Gorges University), Ministry of Education, Yichang 443000, China;

  • 3. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China

    摘要
  • 摘要: 为定量识别边坡的运行状态、预测其失稳风险，基于故障预测与健康管理（PHM）方法，构建边坡安全监测PHM系统框架；以位移为代表，采用基于性能退化的故障预测思想，定义基于边坡实际位移量的故障率和安全储备度；根据边坡滑动失稳特征，结合斋藤迪孝法及神经网络，建立故障率及安全储备度算式，得到性能退化时间曲线，实现边坡位移故障与安全储备的动态识别及预测，为采取相应治理、防范措施提供直接依据。结合某边坡监测信息，阐述边坡PHM系统位移故障预测的实现过程，建立的分析模型及预测结果与实际情况相符。
    Abstract: In order to quantitatively recognize the operation state of a slope and predict its risk of instability, based on the theory of Prognostic and Health Management (PHM), the framework of slope PHM system was studied considering the monitoring situation. Based on the performance degradation fault forecast method, the Failure Rate (FR) and Residual Safety Degree (RSD) were defined based on the actual displacement of slope. According to the characteristics of slope sliding, combining Saito Michitakanori method and neural networks, the slope slip time and the critical displacement were predicted from the monitoring displacements. The FR and RSD formulas were established, and the performance degradation time curve was drawn to realize the quantitative identification and forecast FR and RSD. They could be used to provide direct guidance for appropriate governance and maintenance. According to the monitoring information of a slope, the FR forecast process of displacement for slope PHM system was explained in detail. Its analytical model and forecasted results were in good agreement with the slope actual situation.
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    • 参考文献(22)
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出版历程
  • 收稿日期:  2019-11-13
  • 刊出日期:  2020-07-14

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