本研究利用紅外線熱顯像儀進行結構健康監測，以門型壓克力模型為實驗對象，並利用電熱片做為結構標記點，紀錄結構響應。此外，以加速規進行驗證，發現其平均自然頻率誤差為3.93%，並發現誤差來源可能為空間解析度與採樣頻率的差異。而振型部份以模態置信準則量化其差異，發現MAC值為0.9963，高度一致性。為了確保未來可以將紅外線熱顯像儀應用於結構健康監測，本研究採用低模態即可識別結構破壞的振型曲率法，在第一模態時就成功定位破壞位置。另一方面，為凸顯紅外線熱顯像儀相對於一般光學相機的優勢，設計夜晚、遮蔽物、雲霧環境，證實熱顯像儀可以於視線不佳的環境進行量測。最後，若未來要將此方法應用於大型結構上，勢必會面臨因解析度不足而無法進行全域量測的難題，因此本研究提出分段量測的方法，以振型重組、畸變修正克服此難題，並以三層樓壓克力模型為實驗對象，與加速規比較，其MAC值為0.9810，吻合度極高。

In this study, the infrared thermal imager was used for structural health monitoring. The gate-type acrylic model was used as the experimental object, and the electric heating film was used as the structural marker point to record the structural response. In addition, the acceleration is verified by the acceleration gauge, and the average natural frequency error is found to be 3.93%. It is found that the error source may be the difference between the spatial resolution and the sample rate. The mode shape is quantized by the modal confidence criterion(MAC), and the MAC value is found to be 0.9963 between accerometer and thermal imager, which is highly consistent. In order to ensure that the infrared thermal imager can be applied to structural health monitoring in the future, this model uses a mode shape curvature method (MSCM) that can identify structural damage in a low mode, and successfully locates the damage location in the first mode. On the other hand, in order to highlight the advantages of the infrared thermal imager over the general optical camera, the night, the shelter, and the cloud environment are designed to confirm that the thermal imager can be measured in an environment with poor visibility. Finally, if this method is to be applied to large-scale structures in the future, it will inevitably face the problem of being unable to perform global measurement due to insufficient resolution. Therefore, this study proposes a method of segmentation measurement, which overcomes this by mode reorganization and distortion correction. The problem is that the three-story acrylic model is the experimental object. Compared with the acceleration gauge, the MAC value is 0.9810, and the degree of coincidence is extremely high.

目錄

誌謝 i
中文摘要 ii
英文摘要 iii
目錄 iv
圖目錄 vi
表目錄 x
第一章 簡介 1
1.1 動機 1
1.2 研究背景 3
1.3 研究目的 4
1.4 重要性與貢獻 4
1.5 名詞對照與符號說明 5
第二章 文獻探討 8
2.1 沿革 8
2.2 模態參數監測法 9
2.3 影像量測於結構健康監測 12
2.4 紅外線熱顯像儀應用 16
第三章 研究方法 20
3.1 研究流程 20
3.2 實驗數據量測 21
3.3 影像處理方法 27
3.4 模態分析 31
3.5 破壞定位方法 36
第四章 研究結果 38
4.1 位移驗證結果 38
4.2 模態分析結果 41
4.3 破壞識別結果 44
第五章 討論 46
5.1 位移結果比較 46
5.2 模態分析結果比較 50
5.3 距離探討 54
5.4 環境實驗 55
5.5 應用討論 64
第六章 結論與未來展望 75
6.1 結論 75
6.2 未來展望 77
第七章 參考文獻 78

1.Innovation outlook offshore wind. 2016.
2.Global wind speed rankings,[Online]. Available: http://www.4coffshore.com/windfarms/windspeeds.aspx [accessed June 4 2018].
3.Chou, J.-S., et al., Structural failure simulation of onshore wind turbines impacted by strong winds. Engineering Structures, 2018. 162: p. 257-269.
4.Poozesh, P., et al., Large-area photogrammetry based testing of wind turbine blades. Mechanical Systems and Signal Processing, 2017. 86: p. 98-115.
5.黃仲偉, et al., 影像量測於結構監測之應用. 土木水利, 2016. 43(1): p. 66-74.
6.Beberniss, T.J. and D.A. Ehrhardt, High-speed 3D digital image correlation vibration measurement: Recent advancements and noted limitations. Mechanical Systems and Signal Processing, 2017. 86: p. 35-48.
7.Feng, D. and M.Q. Feng, Vision‐based multipoint displacement measurement for structural health monitoring. Structural Control and Health Monitoring, 2016. 23(5): p. 876-890.
8.Ribeiro, D., et al., Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system. Engineering Structures, 2014. 75: p. 164-180.
9.Martinez-Luengo, M., A. Kolios, and L. Wang, Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm. Renewable and Sustainable Energy Reviews, 2016. 64: p. 91-105.
10.Weijtjens, W., et al., Foundation structural health monitoring of an offshore wind turbine—a full-scale case study. Structural Health Monitoring, 2016. 15(4): p. 389-402.
11.Devriendt, C., et al., Structural health monitoring of offshore wind turbines using automated operational modal analysis. Structural Health Monitoring, 2014. 13(6): p. 644-659.
12.Weng, J.-H., et al., Output-only modal identification of a cable-stayed bridge using wireless monitoring systems. Engineering Structures, 2008. 30(7): p. 1820-1830.
13.Omenzetter, P. and J.M.W. Brownjohn, Application of time series analysis for bridge monitoring. Smart Materials and Structures, 2006. 15(1): p. 129.
14.Magalhães, F., A. Cunha, and E. Caetano, Vibration based structural health monitoring of an arch bridge: from automated OMA to damage detection. Mechanical Systems and Signal Processing, 2012. 28: p. 212-228.
15.He, K. and W. Zhu. Structural damage detection using changes in natural frequencies: theory and applications. in Journal of Physics: Conference Series. 2011. IOP Publishing.
16.Loh, C.-H. and S.-H. Chao, Centralized vs. Pattern-Level Feature Extraction for Structural Damage Detection. Procedia Engineering, 2014. 79: p. 479-489.
17.Fan, W. and P. Qiao, Vibration-based damage identification methods: a review and comparative study. Structural health monitoring, 2011. 10(1): p. 83-111.
18.Messina, A., E. Williams, and T. Contursi, Structural damage detection by a sensitivity and statistical-based method. Journal of sound and vibration, 1998. 216(5): p. 791-808.
19.Fan, W. and P. Qiao, A 2-D continuous wavelet transform of mode shape data for damage detection of plate structures. International Journal of Solids and Structures, 2009. 46(25-26): p. 4379-4395.
20.Hamey, C.S., et al., Experimental damage identification of carbon/epoxy composite beams using curvature mode shapes. Structural Health Monitoring, 2004. 3(4): p. 333-353.
21.Law, S., Z. Shi, and L. Zhang, Structural damage detection from incomplete and noisy modal test data. Journal of Engineering Mechanics, 1998. 124(11): p. 1280-1288.
22.Lee, J.-J. and M. Shinozuka, Real-time displacement measurement of a flexible bridge using digital image processing techniques. Experimental mechanics, 2006. 46(1): p. 105-114.
23.Sarrafi, A., et al., Vibration-based damage detection in wind turbine blades using Phase-based Motion Estimation and motion magnification. Journal of Sound and Vibration, 2018. 421: p. 300-318.
24.Omidalizarandi, M., et al., Accurate vision-based displacement and vibration analysis of bridge structures by means of an image-assisted total station. Advances in Mechanical Engineering, 2018. 10(6): p. 1687814018780052.
25.Ring, E., The discovery of infrared radiation in 1800. The Imaging Science Journal, 2000. 48(1): p. 1-8.
26.Herschel, W., XIV. Experiments on the refrangibility of the invisible rays of the sun. Philosophical Transactions of the Royal Society of London, 1800(90): p. 284-292.
27.Lisowska-Lis, A., S. Mitkowski, and J. Augustyn. Infrared technique and its application in science and engineering in the study plans of students in electrical engineering and electronics. in Proceedings of 2nd world conference on technology and engineering education (WIETE 2011). 2011.
28.Kylili, A., et al., Infrared thermography (IRT) applications for building diagnostics: A review. Applied Energy, 2014. 134: p. 531-549.
29.Mian, A., et al., Fatigue damage detection in graphite/epoxy composites using sonic infrared imaging technique. Composites Science and Technology, 2004. 64(5): p. 657-666.
30.Guo, X. and V. Vavilov, Crack detection in aluminum parts by using ultrasound-excited infrared thermography. Infrared Physics & Technology, 2013. 61: p. 149-156.
31.Talai, S., D. Desai, and P.S. Heyns, Vibration characteristics measurement of beam-like structures using infrared thermography. Infrared Physics & Technology, 2016. 79: p. 17-24.
32.Talai, S.M., D.A. Desai, and S.P. Heyns, Experimentally validated structural vibration frequencies’ prediction from frictional temperature signatures using numerical simulation: A case of laced cantilever beam-like structures. Advances in Mechanical Engineering, 2016. 9(1): p. 1687814016685001.
33.Pixel Connectivity. [cited 2019 06.11]; Available from: https://edoras.sdsu.edu/doc/matlab/toolbox/images/morph12.html.
34.Rainieri, C. and G. Fabbrocino, Operational modal analysis of civil engineering structures. Springer, New York, 2014. 142: p. 143.
35.Brincker, R., L. Zhang, and P. Andersen, Modal identification of output-only systems using frequency domain decomposition. Smart materials and structures, 2001. 10(3): p. 441.
36.Allemang, R.J., The modal assurance criterion–twenty years of use and abuse. Sound and vibration, 2003. 37(8): p. 14-23.
37.Frigui, F., et al., Global methodology for damage detection and localization in civil engineering structures. Engineering Structures, 2018. 171: p. 686-695.
38.Feng, D. and M.Q. Feng, Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection–A review. Engineering Structures, 2018. 156: p. 105-117.
39.Pastor, M., M. Binda, and T. Harčarik, Modal assurance criterion. Procedia Engineering, 2012. 48: p. 543-548.
40.Lee, J.-H., et al., An advanced vision-based system for real-time displacement measurement of high-rise buildings. Smart Materials and Structures, 2012. 21(12): p. 125019.
41.Keith, E.O., Development of an underwater infrared camera to detect manatees. 2005.