Skip to main content
eScholarship
Open Access Publications from the University of California

UC San Diego

UC San Diego Electronic Theses and Dissertations bannerUC San Diego

A multi-scale approach to statistical and model-based structural health monitoring with application to embedded sensing for wind energy

  • Author(s): Taylor, Stuart Glynn
  • et al.
Abstract

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

Main Content
Current View