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基于小波分析的造船用龙门吊损伤识别研究

摘要第4-5页
Abstract第5页
Chapter 1 Introduction第9-21页
    1.1 Importance of structural damage identification and structural health monitoring第9-10页
    1.2 Research background第10-11页
    1.3 State of the art of damage identification methods第11-14页
        1.3.1 Natural frequency based methods第12-13页
        1.3.2 Mode shape based methods第13页
        1.3.3 Flexibility matrix based methods第13页
        1.3.4 Stiffness matrix based methods第13-14页
        1.3.5 Other methods第14页
    1.4 Development of damage identification based on wavelet transform第14-17页
    1.5 Introduction of distributed long-gage fiber optic sensors used in this research第17-18页
    1.6 Research work and structure of this thesis第18-21页
Chapter 2 Theoretical basis第21-49页
    2.1 How wavelet transform is introduced第21-27页
        2.1.1 Applications of Fourier transform第21-24页
        2.1.2 Applications of wavelet transform第24-27页
    2.2 Fourier transform第27-31页
        2.2.1 Fourier series第28-29页
        2.2.2 Continuous Fourier transform第29-30页
        2.2.3 Comparison between Fourier transform and Fourier series第30页
        2.2.4 Discrete Fourier transform第30-31页
    2.3 Short time Fourier transform第31-34页
        2.3.1 Heisenberg uncertainty principle第32-33页
        2.3.2 Resolutions of the STFT第33-34页
    2.4 Continuous wavelet transform第34-38页
        2.4.1 Conditions on mother wavelets第37页
        2.4.2 Resolutions of wavelet transform第37-38页
    2.5 Discrete wavelet transform第38-39页
    2.6 Wavelet packet transform第39-41页
    2.7 Wavelet properties第41-42页
    2.8 Singularity detection principle of wavelet transform第42-45页
        2.8.1 In displacement and acceleration data analysis第42-44页
        2.8.2 In strain time-history signals第44-45页
    2.9 Noise effects第45-47页
        2.9.1 Wavelet based denoising第45-46页
        2.9.2 Simulation of noise第46-47页
    2.10 Conclusions第47-49页
Chapter 3 Damage Identification based on modal displacement第49-71页
    3.1 Wavelet selection第49-50页
        3.1.1 Signal analysis第49-50页
        3.1.2 Wavelet properties第50页
    3.2 Finite element analysis第50-55页
        3.2.1 Numerical Mmodel第51-54页
        3.2.2 Damage scenarios第54-55页
    3.3 Data processing第55-66页
        3.3.1 Boundary effect problem第55-57页
        3.3.2 Comparison between deflection shape and displacement mode shape第57-59页
        3.3.3 Comparison of different wavelets第59-61页
        3.3.4 Selection of deflection mode shape level第61-63页
        3.3.5 Detecting damages by using CWT and selection of the best level for DWT第63-64页
        3.2.6 Multiple damages locations and severities第64-65页
        3.2.7 Calculated deflection mode shape第65-66页
    3.4 Damage identification process第66-67页
    3.5 Validation by scaled gantry crane model第67-69页
    3.6 Conclusions第69-71页
Chapter 4 Damage detection based on modified Wavelet Packet Energy Rate第71-89页
    4.1 Wavelet selection第72-73页
        4.1.1 Signal analysis第72-73页
        4.1.2 Wavelet properties第73页
    4.2 Finite element analysis第73-76页
        4.2.1 Nume rical model第73-75页
        4.2.2 Damage Scenarios第75-76页
    4.3 Data Processing第76-84页
        4.3.1 Comparison of the proposed damage index and conventional indexes第76-77页
        4.3.2 Comparison between hammer excitation and running car excitation第77-79页
        4.3.3 Detectability of damages on simply supported beam第79-80页
        4.3.4 Detectability of multiple damages on the main beam and damage on the column第80页
        4.3.5 Verification of noise effect on the proposed method第80-82页
        4.3.6 Effects of stiffness loss level第82页
        4.3.7 Effects of damage location第82-83页
        4.3.8 Consideration of both damage location and damage level第83-84页
    4.4 Damage identification process第84-85页
    4.5 Validation by scaled gantry crane model第85-86页
    4.6 Conclusions第86-89页
Chapter 5 Experiment on a steel beam第89-95页
    5.1 Design of the experiment第89-90页
    5.2 Eexperiment process第90-92页
    5.3 Damage identification based on the data acquired by the experimental steel beam第92-95页
Chapter 6 Conclusions第95-97页
References第97-101页
Acknowledgements第101-102页
Papers published during master degree第102页

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