Inhalt

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   Titelblatt
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   Acknowledgements
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   Contents
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  Nomenclature
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   Abbreviations
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   Fixed symbols
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   Operators
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   Sets
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   Abstract
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   Kurzfassung
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  1 Introduction
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   1.1 Introduction to Structural Health Monitoring (SHM)
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   1.2 Motivation
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   1.3 Basic idea of online force reconstruction
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   1.4 State of the art
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   1.5 Original contributions
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   1.6 Organization of the thesis
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  2 Theoretical foundations
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  2.1 Structural model construction
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   2.1.1 Second-order structural model
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   2.1.2 Continuous-time state-space structural model
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   2.1.3 Discrete-time state-space structural model
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   2.1.4 Experimental Modal Analysis (EMA)
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   2.1.5 Operational Modal Analysis (OMA)
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   2.1.6 Model updating
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   2.2 Observer
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   2.3 Unknown Input Observer (UIO)
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   2.4 Kalman filter
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   2.5 Kalman-Bucy filter
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   2.6 Real-time executable state and input estimation algorithms
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   2.6.1 Proportional-Integral observer (PI observer)
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   2.6.2 Simultaneous State and Input Estimator (SS&IE)
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   2.6.3 Kalman Filter and a Recursive Least-Squares Estimator (KF+RLSE)
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   2.6.4 Recursive Three-Step Filter (RTSF)
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   2.6.5 Kalman Filter with Unknown Inputs (KF-UI)
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   2.6.6 Augmented Kalman Filter (AKF)
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   2.6.7 Steady-State Kalman Filter and a Least-Squares Estimator
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   2.7 Correlation of the process noise and the measurement noise
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  3 Problem formulation and methodology
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   3.1 Force reconstruction is a kind of inverse problem
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   3.2 Ill-posed nature in force reconstruction
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  3.3 From ill-posedness to well-posedness in online force reconstruction
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   3.3.1 Observability
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   3.3.2 Stability and convergence
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   3.4 Methodology for online force reconstruction with structural modal parameters identified by experimental modal analysis
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   3.5 Methodology for online force reconstruction with structural modal parameters identified by operational modal analysis
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   3.6 Methodology for the reconstruction of a distributed force with unknown spatial distribution
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  4 Proposed algorithm modifications
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   4.1 Simultaneous State and Input Estimator for Linear systems (SS&IE_L)
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   4.2 Steady-state of KF+RLSE
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   4.3 Generalized Kalman filter with unknown inputs (G-KF-UI)
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   4.3.1 Difference and relationship between the KF-UI and the RTSF
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   4.3.2 Proposed modifications to the KF-UI
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   4.3.3 Generalized form of the KF-UI
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   4.3.4 Steady-state of G-KF-UI
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   4.4 Modified Steady-State Kalman Filter and a Least-Squares Estimator
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  5 Study on application-oriented algorithm selection
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   5.1 From mathematics to practical requirements
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   5.1.1 Assumption on inputs
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   5.1.2 Assumption on initial state estimate
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   5.1.3 Assumption on direct feed-through
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   5.1.4 Mathematical conditions on system matrices
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  5.2 Benchmark study
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   5.2.1 Introduction to the benchmark structure
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   5.2.2 Structural model construction for the benchmark structure
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   5.2.3 Considered forces
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   5.2.4 Test using PI observer
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   5.2.5 Test using KF+RLSE
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   5.2.6 Test using SSIE_L
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   5.2.7 Test using AKF
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   5.2.8 Test using KF-UI and G-KF-UI
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   5.2.9 Test using SSKF+LSE and MSSKF+LSE
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   5.2.10 Summary
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   5.3 Proposed guidance for algorithm selection
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  6 Practical application to the Canton Tower
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  6.1 Introduction
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   6.1.1 Canton Tower
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   6.1.2 SHM system for the Canton Tower
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   6.1.3 SHM benchmark study for the Canton Tower
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   6.1.4 Organization of this chapter
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   6.2 Methodology for wind load reconstruction for the Canton Tower
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  6.3 Operational modal analysis for the Canton Tower
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   6.3.1 Sensor deployment and field measurements
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   6.3.2 Stationarity test
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   6.3.3 OMA results
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   6.3.4 Discussion
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  6.4 Model updating for the Canton Tower
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   6.4.1 Modified reduced-order FE model of the Canton Tower
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   6.4.2 Model updating method
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   6.4.3 Model updating results
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   6.5 Algorithm selection
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   6.6 Wind load reconstruction for the Canton Tower
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   6.6.1 Reconstruction of the wind load
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   6.6.1.1 Reconstruction of mean wind load
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   6.6.1.2 Reconstruction of the fluctuating wind load
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   6.6.2 Validation of the reconstructed fluctuating wind load
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   6.7 Summary
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  7 Summary and outlook
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   7.1 Summary
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   7.2 Outlook
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   Bibliography
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   A Derivation of the SS&IE_L
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   B Proof of the equivalence of filter equations of the KF-UI and the RTSF