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A Multi-scale Approach to Statistical and Model-based Structural Health Monitoring with Application to Embedded Sensing for Wind Energy.

机译：基于统计和基于模型的结构健康监测的多尺度方法及其在风能嵌入式传感中的应用。

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摘要

This dissertation presents a systems-level approach to multi-scale structural health monitoring (SHM) with specific focus on wind turbine rotor blades, combining innovative sensing platforms for incipient damage detection with state estimation for structural performance assessment. The practical implementation of this approach rests in three areas: hardware development and deployment for embedded data acquisition; demonstration of incipient damage detection using embedded systems for active-sensing SHM, including an in-depth assessment of sensor diagnostics; and development of a nonlinear observer for state and loads estimation applied to a geometrically nonlinear beam model.;Structural Health Monitoring is generally defined as the development of an in-situ damage assessment capability, and when combined predictive loading and failure models, enables risk-informed models for decision-making. These decision models require contributions from a wide variety of technology areas. Sensing systems (in many cases, capable of providing multiple data types) must be developed specifically to provide the data necessary for structural damage detection and performance assessment. A means of sensor diagnostics is necessary to provide confidence in the recorded data. Statistical modeling and classification feed the development of optimal detectors necessary to ascertain the presence, location, and severity of damage. Methods of state estimation are needed to map kinematic measurements to physical performance metrics. A probabilistic representation of future loads applied to a structural model enables an assessment of the structure's future performance. Finally, a cost model is combined with a probabilistic risk assessment, given the detectors' output and the structure's estimated future performance, to render the risk-minimizing decision. This dissertation presents key contributions among the underpinnings of this ultimate decision model: (1) embedded sensor development and deployment; (2) sensor diagnostics for active-sensing methods; (3) an assessment of incipient damage detection performance for large-scale composite structures; and (4) the development and application of a state observer, demonstrated in the specific case of a geometrically nonlinear beam model.
机译：本文提出了一种系统级的多尺度结构健康监测（SHM）方法，特别关注风力涡轮机转子叶片，结合了用于初期损伤检测的创新传感平台和用于结构性能评估的状态估计。这种方法的实际实现取决于三个领域：嵌入式数据采集的硬件开发和部署；演示使用嵌入式系统进行主动感应SHM进行的早期损坏检测，包括对传感器诊断的深入评估； ；以及开发用于状态和载荷估计的非线性观察器的方法，并将其应用于几何非线性梁模型。；结构健康监测通常定义为就地破坏评估能力的开发，并且当组合预测载荷和破坏模型时，可以实现风险-明智的决策模型。这些决策模型需要来自各种技术领域的贡献。必须专门开发传感系统（在许多情况下，能够提供多种数据类型），以提供结构损伤检测和性能评估所需的数据。必须提供一种传感器诊断方法，以确保对记录的数据有信心。统计建模和分类为确定损坏的存在，位置和严重性提供了必要的最佳检测器。需要状态估计的方法来将运动学测量结果映射到物理性能指标。应用到结构模型中的未来载荷的概率表示可以评估结构的未来性能。最后，将成本模型与概率风险评估相结合，给出检测器的输出和结构的估计未来性能，以做出最小化风险的决策。本文提出了这一最终决策模型的主要贡献：（1）嵌入式传感器的开发和部署； （2）主动诊断方法的传感器诊断； （3）评估大型复合结构的早期损坏检测性能； （4）状态观测器的开发和应用，在几何非线性光束模型的特定情况下得到了证明。

著录项

  • 作者

    Taylor, Stuart Glynn.;

  • 作者单位

    University of California, San Diego.;

  • 授予单位 University of California, San Diego.;
  • 学科 Alternative Energy.;Engineering Mechanical.;Engineering Civil.;Engineering General.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 198 p.
  • 总页数 198
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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