請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87637完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃心豪 | zh_TW |
| dc.contributor.advisor | Hsin-Haou Huang | en |
| dc.contributor.author | 林佳業 | zh_TW |
| dc.contributor.author | Chia-Yeh Lin | en |
| dc.date.accessioned | 2023-06-21T16:04:14Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-06-21 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-02-15 | - |
| dc.identifier.citation | [1] "郁志榮：日本工匠精神失傳 建造貨船誰人敢用." https://read01.com/zh-hk/zPxo2kD.html#.Y6xi3XZByUk.
[2] D. Feng and M. Q. Feng, "Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection–A review," Engineering Structures, vol. 156, pp. 105-117, 2018. [3] F. Bonnín-Pascual and A. Ortiz, "Detection of Cracks and Corrosion for Automated Vessels Visual Inspection," in CCIA, 2010, pp. 111-120. [4] F. Bonnín Pascual, "Contributions to robot-based vessel visual inspection," Universitat de les Illes Balears, doctoral thesis, 2017. [5] W. Peters and W. Ranson, "Digital imaging techniques in experimental stress analysis," Optical engineering, vol. 21, no. 3, p. 213427, 1982. [6] B. Pan, A. Asundi, H. Xie, and J. Gao, "Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements," Optics and Lasers in Engineering, vol. 47, no. 7-8, pp. 865-874, 2009. [7] B. Pan, K. Li, and W. Tong, "Fast, Robust and Accurate Digital Image Correlation Calculation Without Redundant Computations," Experimental Mechanics, vol. 53, no. 7, pp. 1277-1289, 2013. [8] F. Chen, X. Chen, X. Xie, X. Feng, and L. Yang, "Full-field 3D measurement using multi-camera digital image correlation system," Optics and Lasers in Engineering, vol. 51, no. 9, pp. 1044-1052, 2013. [9] N. V. Dinh, G. M. Hassan, A. V. Dyskin, and C. MacNish, "Digital image correlation for small strain measurement in deformable solids and geomechanical structures," in 2015 IEEE International Conference on Image Processing (ICIP), 2015: IEEE, pp. 3324-3328. [10] F. Zhu, P. Bai, J. Zhang, D. Lei, and X. He, "Measurement of true stress–strain curves and evolution of plastic zone of low carbon steel under uniaxial tension using digital image correlation," Optics and Lasers in Engineering, vol. 65, pp. 81-88, 2015. [11] M. Mehdikhani, M. Aravand, B. Sabuncuoglu, M. G. Callens, S. V. Lomov, and L. Gorbatikh, "Full-field strain measurements at the micro-scale in fiber-reinforced composites using digital image correlation," Composite Structures, vol. 140, pp. 192-201, 2016. [12] D. Hang, G. M. Hassan, C. MacNish, and A. Dyskin, "Characteristics of color digital image correlation for deformation measurement in geomechanical structures," in 2016 international conference on digital image computing: techniques and applications (DICTA), 2016: IEEE, pp. 1-8. [13] L. Yu and B. Pan, "Single-camera high-speed stereo-digital image correlation for full-field vibration measurement," Mechanical Systems and Signal Processing, vol. 94, pp. 374-383, 2017. [14] J. Li, X. Dan, W. Xu, Y. Wang, G. Yang, and L. Yang, "3D digital image correlation using single color camera pseudo-stereo system," Optics & Laser Technology, vol. 95, pp. 1-7, 2017. [15] D. Solav, K. M. Moerman, A. M. Jaeger, K. Genovese, and H. M. Herr, "MultiDIC: An open-source toolbox for multi-view 3D digital image correlation," IEEE Access, vol. 6, pp. 30520-30535, 2018. [16] A. Acciaioli, G. Lionello, and M. Baleani, "Experimentally achievable accuracy using a digital image correlation technique in measuring small-magnitude (< 0.1%) homogeneous strain fields," Materials, vol. 11, no. 5, p. 751, 2018. [17] L. Yu, R. Tao, and G. Lubineau, "Accurate 3D shape, displacement and deformation measurement using a smartphone," Sensors, vol. 19, no. 3, p. 719, 2019. [18] J. M. Vasco‐Olmo, F. A. Diaz Garrido, F. V. Antunes, and M. James, "Plastic CTOD as fatigue crack growth characterising parameter in 2024‐T3 and 7050‐T6 aluminium alloys using DIC," Fatigue & Fracture of Engineering Materials & Structures, vol. 43, no. 8, pp. 1719-1730, 2020. [19] B. Chen and B. Pan, "Camera calibration using synthetic random speckle pattern and digital image correlation," Optics and Lasers in Engineering, vol. 126, 2020. [20] M. Babaeeian and M. Mohammadimehr, "Investigation of the time elapsed effect on residual stress measurement in a composite plate by DIC method," Optics and Lasers in Engineering, vol. 128, 2020. [21] B. Chen, K. Genovese, and B. Pan, "Calibrating large-FOV stereo digital image correlation system using phase targets and epipolar geometry," Optics and Lasers in Engineering, vol. 150, 2022. [22] J.-W. Park, J.-J. Lee, H.-J. Jung, and H. Myung, "Vision-based displacement measurement method for high-rise building structures using partitioning approach," Ndt & E International, vol. 43, no. 7, pp. 642-647, 2010. [23] B. LeBlanc, C. Niezrecki, P. Avitabile, J. Chen, and J. Sherwood, "Damage detection and full surface characterization of a wind turbine blade using three-dimensional digital image correlation," Structural Health Monitoring, vol. 12, no. 5-6, pp. 430-439, 2013. [24] V. G. M. Annamdas, Y. Chew, J. H. L. Pang, H. J. Hoh, K. Zhou, and B. Song, "Fatigue growth analysis of pre induced surface defects using piezoelectric wafer based impedance method and digital image correlation system," Journal of Nondestructive Evaluation, vol. 33, no. 3, pp. 413-426, 2014. [25] B. Omondi, D. G. Aggelis, H. Sol, and C. Sitters, "Improved crack monitoring in structural concrete by combined acoustic emission and digital image correlation techniques," Structural Health Monitoring, vol. 15, no. 3, pp. 359-378, 2016. [26] L. Tian, X. Zhang, and B. Pan, "Cost-Effective and Ultraportable Smartphone-Based Vision System for Structural Deflection Monitoring," Journal of Sensors, vol. 2021, 2021. [27] S. L. Lau, I. König, K. David, B. Parandian, C. Carius-Düssel, and M. Schultz, "Supporting patient monitoring using activity recognition with a smartphone," in 2010 7th International symposium on wireless communication systems, 2010: IEEE, pp. 810-814. [28] S. L. Lau and K. David, "Movement recognition using the accelerometer in smartphones," in 2010 Future Network & Mobile Summit, 2010: IEEE, pp. 1-9. [29] H. Ketabdar and T. Polzehl, "Fall and emergency detection with mobile phones," in Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility, 2009, pp. 241-242. [30] J. Reilly, S. Dashti, M. Ervasti, J. D. Bray, S. D. Glaser, and A. M. Bayen, "Mobile phones as seismologic sensors: Automating data extraction for the iShake system," IEEE Transactions on Automation Science and Engineering, vol. 10, no. 2, pp. 242-251, 2013. [31] S. Barone, P. Neri, A. Paoli, and A. V. Razionale, "Low-frame-rate single camera system for 3D full-field high-frequency vibration measurements," Mechanical Systems and Signal Processing, vol. 123, pp. 143-152, 2019. [32] P. Poozesh, J. Baqersad, C. Niezrecki, P. Avitabile, E. Harvey, and R. Yarala, "Large-area photogrammetry based testing of wind turbine blades," Mechanical Systems and Signal Processing, vol. 86, pp. 98-115, 2017. [33] J. Littell, "Large field digital image correlation used for full-scale aircraft crash testing: methods and results," in International Digital Imaging Correlation Society: Springer, 2017, pp. 235-239. [34] P. Poozesh, A. Sabato, A. Sarrafi, C. Niezrecki, P. Avitabile, and R. Yarala, "Multicamera measurement system to evaluate the dynamic response of utility‐scale wind turbine blades," Wind Energy, vol. 23, no. 7, pp. 1619-1639, 2020. [35] X. Chen et al., "Fatigue testing of a 14.3 m composite blade embedded with artificial defects–damage growth and structural health monitoring," Composites Part A: Applied Science and Manufacturing, vol. 140, p. 106189, 2021. [36] V. Srivastava and J. Baqersad, "An optical-based technique to obtain operating deflection shapes of structures with complex geometries," Mechanical Systems and Signal Processing, vol. 128, pp. 69-81, 2019. [37] D. Gorjup, J. Slavič, and M. Boltežar, "Frequency domain triangulation for full-field 3D operating-deflection-shape identification," Mechanical Systems and Signal Processing, vol. 133, p. 106287, 2019. [38] Y.-H. Chang, W. Wang, J.-Y. Chang, and J. E. Mottershead, "Compressed sensing for OMA using full-field vibration images," Mechanical Systems and Signal Processing, vol. 129, pp. 394-406, 2019. [39] D. Uehara and J. Sirohi, "Full-field optical deformation measurement and operational modal analysis of a flexible rotor blade," Mechanical Systems and Signal Processing, vol. 133, p. 106265, 2019. [40] Z. Zhang, "A flexible new technique for camera calibration," IEEE Transactions on pattern analysis and machine intelligence, vol. 22, no. 11, pp. 1330-1334, 2000. [41] D. Atkinson and T. Becker, "A 117 Line 2D Digital Image Correlation Code Written in MATLAB," Remote Sensing, vol. 12, no. 18, 2020. [42] P. Bing, X. Hui-Min, X. Bo-Qin, and D. Fu-Long, "Performance of sub-pixel registration algorithms in digital image correlation," Measurement Science and Technology, vol. 17, no. 6, p. 1615, 2006. [43] 陳亮至, “建構立體數位影像相關法之基礎理論並應用於結構靜態與動態三維變形精密量測,” 國立台灣大學, 2016. [44] J. Blaber, B. Adair, and A. Antoniou, "Ncorr: Open-Source 2D Digital Image Correlation Matlab Software," Experimental Mechanics, vol. 55, no. 6, pp. 1105-1122, 2015. [45] "Detection of ArUco Markers." https://docs.opencv.org/4.x/d5/dae/tutorial_aruco_detection.html. [46] R. Hartley and A. Zisserman, Multiple view geometry in computer vision. Cambridge university press, 2003. [47] B. Pan, L. Yu, D. Wu, and L. Tang, "Systematic errors in two-dimensional digital image correlation due to lens distortion," Optics and lasers in engineering, vol. 51, no. 2, pp. 140-147, 2013. [48] Z. Wang, H. Li, J. Tong, and J. Ruan, "Statistical analysis of the effect of intensity pattern noise on the displacement measurement precision of digital image correlation using self-correlated images," Experimental Mechanics, vol. 47, no. 5, pp. 701-707, 2007. [49] B. Pan, "Bias error reduction of digital image correlation using Gaussian pre-filtering," Optics and Lasers in Engineering, vol. 51, no. 10, pp. 1161-1167, 2013. [50] 陳楚云, “基於數值分析與深度學習之感測器佈設最佳化與結構健康監測方法建立,” 國立台灣大學, 2022. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87637 | - |
| dc.description.abstract | 本文以影像視覺方法進行結構裂縫破壞檢測，提出固定式和非固定相機檢測方法，以全域應變場集中位置有效定位結構裂縫，達到破壞檢測目的，為船體結構監測提出一個全新的方向。
在固定式相機監測方法中，以數位影像相關法建立全域應變場監測材料在降伏前所發生的應變，經過船體局部縮尺結構進行實驗驗證，驗證結果皆可在裂縫發生前預測破壞位置。為了更貼近實際應用層面，本研究探討固定式方法在船體黑暗環境中的使用情況，並進行參數討論提出有效之改善方法，其改善方法透過實驗驗證可行性。 為了達到監測方法不須長期架設固定相機的目的，本研究提出一非固定式相機監測方法，以自主開發之標記式與單應式矩陣演算法校正非固定姿態影像為固定影像。經過實驗驗證，本研究所開發之演算法在不同的相機姿態下成功於結構裂縫破壞前預測並定位裂縫位置，此外亦討論了標記點貼附於結構的使用情境，並透過實驗驗證其貼附式方法之裂縫檢測能力。 | zh_TW |
| dc.description.abstract | In this study, we proposed an image-based method aim to inspect structural crack damage. The method has been separated into fixed and mobile mode camera methods, which both can locate crack position at the concentration position by the structure full-field strain. With the outstanding behavior of proposed damage inspection method, it can point ship structure monitoring to a new direction.
In the fixed camera inspection method, the digital image correlation method is used to establish a global strain field, which can measure the strain of the material before yielding. The crack prediction ability of the method is verified by the lab-scale partial ship structure experiment. The verification results can predict the damage position before the occurrence of crack. In order to get closer to the practical application, this study discuss the fixed method in dark environment. This study also proposes effective methods to improve the situations, and the methods had been verified the feasibility by experiment results. In order to achieve the requirement of crack inspection but no need to install the fixed camera, this study proposes a non-fixed camera monitoring method, which can transform the non-fixed pose image into a fixed image by the developed marker-based and homography matrix algorithm. As the result of the lab-scale structure experiment, the developed algorithm successfully predicted and located the crack location before the structural crack occurrence by different camera poses. This study also discusses the situation which the markers is needed to attach on the structure, and the crack inspection ability of attached method had been verified by experiment. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-06-21T16:04:14Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-06-21T16:04:14Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii Abstract iv 目錄 v 圖目錄 viii 表目錄 xiv 第一章 研究簡介 1 1.1 動機 1 1.2 研究背景 2 1.3 研究目的 2 1.4 重要性與貢獻 3 1.5 名詞對照與符號說明 4 1.5.1 英文專有名詞與中文翻譯對照 4 1.5.2 符號說明表 5 第二章 文獻探討 7 2.1 文獻回顧 7 2.2 傳統船體影像視覺裂縫辨識方法 7 2.3 數位影像相關演算法研究 8 2.4 結構系統識別結果 9 2.5 影像方法對大型結構進行結構健康監測 10 第三章 研究方法 12 3.1 研究流程 12 3.2 相機數學模型簡介 13 3.2.1 世界座標與相機座標間的線性轉換 14 3.2.2 相機座標與影像平面間的投影轉換 15 3.3 數位影像相關法簡介 16 3.3.1 數位影像相關法位移計算 17 3.3.2 反向合成高斯牛頓法 19 3.3.3 數位影像相關法應變計算 23 3.4 標記式與單應式矩陣演算法 24 3.4.1 標記點檢測－ARUCO辨識 25 3.4.2 特徵區域單應式矩陣轉換 26 3.5 影像前處理方法及實驗 30 3.5.1 影像之扭曲現象 30 3.5.2 影像扭曲校正測試 31 3.5.3 影像自相關實驗 32 3.5.4 影像雜訊濾波實驗設計 33 3.6 結構變形位移追蹤以及應力集中預測裂縫破壞實驗 34 3.6.1 結構變形檢測之特徵點位移追蹤實驗驗證 35 3.6.2 全域應變場應變精度實驗 36 3.6.3 船體局部縮尺結構試驗 38 3.7 影像環境參數討論 46 3.7.1 黑暗環境對影像量測精度影響實驗 46 3.8 標記式與單應式矩陣演算法結構裂縫破壞檢測 46 3.8.1 校正影像自相關實驗 47 3.8.2 校正影像精度驗證實驗 47 3.8.3 船體局部結構裂縫檢測實驗 48 3.8.4 結構標記貼附裂縫檢測可行性實驗與討論 49 3.9 實驗儀器及設備 50 第四章 研究結果 52 4.1 影像前處理實驗結果 52 4.1.1 影像濾波實驗結果 52 4.1.2 影像扭曲校正實驗結果 53 4.2 結構變形位移追蹤以及以應力集中預測裂縫實驗結果 54 4.2.1 以消費型相機進行位移追蹤實驗結果 54 4.2.2 數位影像相關法精度驗證實驗 56 4.2.3 類別A簡單局部結構破壞預測結果 58 4.2.4 類別B局部縮尺結構破壞預測結果 62 4.2.5 類別C局部縮尺結構應變規數據比對以及破壞預測結果 68 4.3 標記式與單應式矩陣演算法結構破壞檢測實驗結果 70 4.3.1 演算法精度驗證實驗結果 70 4.3.2 演算法船體局部結構裂縫檢測實驗 73 第五章 討論 79 5.1 環境照度參數影像精度探討實驗結果 79 5.2 結構標記貼附裂縫檢測可行性實驗與討論結果 81 5.2.1 結構標記貼附量測限制討論 81 5.2.2 結構標記貼附式裂縫破壞檢測可行性實驗結果與討論 82 第六章 結論與未來展望 86 6.1 結論 86 6.2 未來展望 87 第七章 參考文獻 88 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 全域變形量測 | zh_TW |
| dc.subject | 數位影像相關法 | zh_TW |
| dc.subject | 非接觸式檢測 | zh_TW |
| dc.subject | 裂縫破壞檢測 | zh_TW |
| dc.subject | 單應式矩陣演算法 | zh_TW |
| dc.subject | non-contact detection | en |
| dc.subject | digital image correlation method | en |
| dc.subject | full-field deformation measurement | en |
| dc.subject | homography matrix algorithm | en |
| dc.subject | crack damage detection | en |
| dc.title | 基於標記式與單應式矩陣演算法識別結構應力集中應用於裂縫破壞檢測 | zh_TW |
| dc.title | Stress concentration applied on crack inspection based on marker-based and homography matrix algorithm | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 宋家驥;張恆華;李佳翰;黃勝翊 | zh_TW |
| dc.contributor.oralexamcommittee | Chia-Chi Sung;Herng-Hua Chang;Jia-Han Li;Hseng-Ji Huang | en |
| dc.subject.keyword | 數位影像相關法,非接觸式檢測,裂縫破壞檢測,單應式矩陣演算法,全域變形量測, | zh_TW |
| dc.subject.keyword | digital image correlation method,non-contact detection,crack damage detection,homography matrix algorithm,full-field deformation measurement, | en |
| dc.relation.page | 92 | - |
| dc.identifier.doi | 10.6342/NTU202300404 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-02-15 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工程科學及海洋工程學系 | - |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-1.pdf 未授權公開取用 | 8.11 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。