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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃心豪 | |
dc.contributor.author | Jin Tao | en |
dc.contributor.author | 陶金 | zh_TW |
dc.date.accessioned | 2021-06-17T09:08:35Z | - |
dc.date.available | 2022-12-03 | |
dc.date.copyright | 2019-12-03 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-11-05 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74839 | - |
dc.description.abstract | 感測器最佳化佈設的目的是為了找到能夠盡可能多的獲取結構健康監測所需要的結構動力資訊的感測器佈設方案。本研究提出以多目標演化式演算法結合粒子群演算法的思路,以模態振型線性獨立,採集數據的冗餘性和振動響應訊號作為三個優化目標,同時結合MAC矩陣進行感測器數量的預估,最後提出一種基於距離衡量的多目標決策的策略作為最終解的選擇方案。接著研究了將該方法用於610公尺高的廣州塔結構上和其它文獻中的結果做對比,結果表明提出的方法在多目標情況下有更均衡的表現且在所提出的指標下表現更好。然後在實驗室架設一個三層框架結構,以加速度感測器驗證多目標最佳化方案的有效性,實驗結果顯示結構模態資訊都能夠完整獲得,且在和其他方法的結果比較與計算模態分析結果相吻合。最後,將感測器最佳化佈設分析流程應用於實際風機有限元模型的分析。 | zh_TW |
dc.description.abstract | The objective of optimal sensor placement (OSP) is to obtain a sensor layout that gives as much information of the dynamic system as possible in structural health monitoring (SHM). In this paper, a modified multi-objective evolutionary algorithm combined with particle swarm optimization (PSO) is adopted. The three optimization objectives are linear independence of mode shapes, dynamic information redundancy and vibration response signal strength. Moreover, a multi-objective decision-making (MODM) strategy based on distance measurement is proposed as the final solution selection method. Then, the technique is applied to the 610-meter-high Guangzhou Tower and compared with the results in other literatures. The results show that the proposed method has a more balanced performance under multi-objective conditions and performs better under the proposed MODM strategy. Next, a three-story frame structure is set up in the laboratory to valid the effectiveness of the multi-objective optimization method by acceleration sensors. The experimental results show that the structural modal information can be obtained completely, and the results of comparison with those of other methods are consistent with the results of computational modal analysis. Finally, the OSP analysis process is applied to the analysis of the offshore wind support structure finite element model. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T09:08:35Z (GMT). No. of bitstreams: 1 ntu-108-R06525091-1.pdf: 5393836 bytes, checksum: 1d48ab2a290a32048a73669e0aa5fef0 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 Acknowledgments ii 中文摘要 iii ABSTRACT iv 目錄 Table of Contents v 圖目錄 List of Figures viii 表目錄 List of Tables x 第一章 簡介/緒論 Introduction 1 1.1 緣起/動機(Motivation) 1 1.2 研究背景(Research Background) 3 1.3 研究目的(Objective) 5 1.4 重要性與貢獻(Significance and Contributions) 5 1.5 名詞對照與符號說明(Terminology and Nomenclature) 6 1.5.1 英文專有名詞與中文翻譯對照(Terminology) 6 1.5.2 符號說明表(Nomenclature) 8 第二章 文獻探討 Literature Reviews 10 2.1 健康監測中的感測器佈設方法(OSP Methods in Structural Health Monitoring) 10 2.1.1 感測器佈設問題的結構動力學模型(Dynamic Model of OSP) 10 2.1.2 模態動能法(Modal Kinetic Energy, MKE) 11 2.1.3 特徵向量乘積法(Eigenvector Component Product, ECP)和模態分量加和法(Mode Shape Summation Plot, MSSP) 11 2.1.4 原點留數法(Drive Point Residue, DPR) 12 2.1.5 有效獨立法(Effective Independence) 13 2.1.6 模態矩陣的QR分解法(QR Decomposition, QRD) 14 2.1.7 minMAC法(minMAC Algorithm) 15 2.1.8 奇異值分解法(Singular Value Decomposition, SVD) 15 2.1.9 空間域採樣法(Space Domain Sampling) 16 2.1.10 基於信息理論的方法(Information-based Method) 17 2.2 最佳化感測器佈設與演化式算法(OSP and Evolutionary Computation) 18 2.2.1 基於仿生學啟發的算法(Biology-based Algorithm) 19 2.2.2 基於物理學啟發的算法(Physics-based Algorithm) 30 2.2.3 基於地理學的演算法(Geography-based Algorithm) 32 2.3 多目標演化式演算法(Multiobjective Evolutionary Algorithm, MOEA) 32 2.3.1 基於支配關係的方法(Dominance-based Method) 35 2.3.2 基於聚集函數的方法(Aggregation-based Method) 38 2.3.3 基於評價指標的方法(Indicator-based Method) 41 第三章 方法 Methods/Methodology 45 3.1 研究架構與流程(Conceptual Framework and Study Process) 45 3.2 模型縮減(Model Reduction) 46 3.2.1 Guyan靜態縮減(Guyan Static Reduction ) 46 3.2.2 改進的縮減方法(Improved Reduced System, IRS) 47 3.2.3 迭代式縮減方法(Iterated Improved Reduced System, IIRS) 48 3.3 基於MAC矩陣的感測器數量預估(The Number of Sensors Estimation) 49 3.4 目標函數(Objective Function Formulation) 50 3.4.1 模態振型線性獨立(Linear Independence of Mode Shapes) 51 3.4.2 採集數據的冗餘性最小(Minimize Dynamic Information Redundancy) 52 3.4.3 振動響應訊號最強(Maximize Vibration Response Signal Strength ) 52 3.5 多目標演算法(Discrete Multi-objective Optimization Algorithm) 53 3.5.1 粒子參數定義(Definition of Discrete Position and Velocity) 54 3.5.2 離散粒子狀態更新(Discrete Particle Status Updating) 55 3.5.3 演算法框架(Framework of Algorithm) 56 3.5.4 初始化(Particle Swarm Initialization) 57 3.5.5 複製和選擇(Mating Selection) 59 3.5.6 更新(Environmental Selection) 61 3.5.7 精英解的保存和選擇(Elite Solution Archive and Selection) 62 3.5.8 參數設置(Experimental Settings) 64 3.6 多目標決策(Multi Objective Decision Making, MODM) 64 第四章 結果與討論 Results and Discussions 67 4.1 廣州塔(Theory Comparison) 67 4.2 實驗室縮尺模型實驗有效性驗證(Experimental Validation) 78 4.3 風機支撐結構佈署方案(Deployment Application) 89 第五章 結論與未來展望 Conclusions and Future Work 96 5.1 結論(Conclusions) 96 5.2 未來展望(Future Work) 98 參考文獻 Reference 100 附錄 Appendix 109 附錄A (Appendix A) 109 | |
dc.language.iso | zh-TW | |
dc.title | 應用多目標演化式演算法於離岸風機健康監測系統之加速度感測器最佳化佈設方法研究 | zh_TW |
dc.title | A Multi-objective Approach to Optimal Acceleration Sensor Placement for Vibration Test in Global Level Offshore Wind Health Monitoring | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 宋家驥,張恆華,林宗岳 | |
dc.subject.keyword | 結構健康監測,感測器最佳化佈設,多目標優化,演化式演算法,模態測試, | zh_TW |
dc.subject.keyword | Structural Health Monitoring,Optimal Sensor Placement,Multi-objective Optimization Problem,Evolutionary Algorithm,Modal Test, | en |
dc.relation.page | 109 | |
dc.identifier.doi | 10.6342/NTU201800261 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-11-06 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
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