請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41993
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張國鎮(Kuo-Chun Chang) | |
dc.contributor.author | Wei-Sung Lin | en |
dc.contributor.author | 林偉淞 | zh_TW |
dc.date.accessioned | 2021-06-15T00:40:58Z | - |
dc.date.available | 2010-01-11 | |
dc.date.copyright | 2010-01-11 | |
dc.date.issued | 2008 | |
dc.date.submitted | 2009-12-23 | |
dc.identifier.citation | 1. R.D. Begg, A.C. Mackenzie, C.J. Dodds and O. Loland, “Structural Integrity Monitoring Using Digital Processing of Vibration Signals”, Proceedings of the 8th Annual Offshore Technology Conference, Houston, TX, pp. 305-311. (1976).
2. William M. Bolstad, “Introduction to Bayesian Statistics”, 2nd Edition, Wiley-Interscience, John Wiley & Sons, New York, pp 275. (2007). 3. Christopher Chatfield, “The Analysis of Time Series: An Introduction”, 3rd Edition, Chapman and Hall, New York (1984). 4. Peter C. Chang, Alison Flatau, and S. C. Liu, “Review Paper: Health Monitoring of Civil Infrastructure”, Journal of Structure Health Monitoring, Vol. 2, No. 3, pp. 257-267. (2003). 5. Scott W. Doebling, Charles R. Farrar, and Michael B. Prime, “A Summary Review of Vibration-Based Damage Identification Methods”, Shock and Vibration Digest. (1998). 6. S.W. Doebling, “Damage Detection and Modal Refinement Using Elemental Stiffness Perturbations with Constrained Connectivity”, Proceedings of AIAA/ASME/AHS Adaptive Structure Forum, AIAA-96-1307, pp. 360-370. (1996). 7. J. Durbin, S. J. Koopman, “Time Series Analysis by State Space Methods”, Oxford Statistical Science Series, Oxford University Press, New York. (2001). 8. C. R. Farrar, Duffey T. A., S. W. Doebling and Nix D. A., “A Statistical Health Monitoring Recognition Paradigm for Vibration-base Structural Health Monitoring”, Proceeding 2nd International Workshop on Structural Health Monitoring, Stanford, CA, September 8-10, pp 764-773. (2000). 9. M. I.. Friswell and J. E. T. Penny, “Is Damage Location Using Vibration Measurement Practical”, Euro-mech 365 International Workshop:damas 97, Structural Damage Assessment Using Advanced Signal Processing Procedures, Sheffield, UK, June/July. (1997). 10. A. D. Keller, M. Schummer, L. Hood, W. L. Ruzzo, “Bayesian Classification of DNA Array Expression Data,” Department of Computer Science and Engineering, University of Washington, Technical Report UW-CSE-2000-08-01, Seattle. (2000). 11. J. Kudva, N. Munir and P. Tan, “Damage Detection in Smart Structures Using Neural Networks and Finite Element Analysisl”, Proceedings of ADPA/AIAA/ASME/SPIE Conference on Active Materials and Adaptive Structures, pp. 559-608. (1991). 12. J. P. Lynch, Y. Wang, K. C. Lu, T. C. Hou, and C. H. Loh,”Post-Seismic Damage Assessment of Steel Structures Instrumented With Self-Interrogating Wireless Sensors,” Proceedings of the 8th National Conference on Earthquake Engineering (8NCEE), San Francisco, CA, April 18 - 21, (2006). 13. L. Ljung, “System Identification Theory for the User”, Englewood Cliffs, Nj: Prentice-Hall.(1987). 14. P. L. Liu, “Identification and Damage Detection of Trusses Using Modal Data”, Journal of Structural Engineering, ASCE, 121(4), pp. 599-608.(1995). 15. E. Straser and A.S. Kiremidjian, “A Modular Wireless Damage Monitoring System for Structures,” The John A. Blume Earthquake Engineering Center Technical Report No. 128, Department of Civil and Environmental Engineering, Stanford University, Stanford, California (1998). 16. H. Sohn and K. Law,“A Bayesian Probabilistic Approach to Damage Detection for Civil Structures.”, Report No. 131, Department of Civil and Environmental Engineering, Standard University.(1999). 17. H. Sohn, C. R. Farrar, “Damage Diagnosis Using Time Series Analysis of Vibration Signals,” Journal of Smart Materials and Structures, 10(3), pp. 446-451. (2001). 18. H. Sohn, C. R. Farrar, F. M. Hemez, D. D. Shunk, S. W. Stinemates, B. R. Nadler and J. J. Czanecki, “A Review of Structural Health Monitoring Literature from 1996-2001,” Los Alamos National Laboratory report LA-13976-MS. (2004). 19. James C. Spall, “Bayesian Analysis of Time Series and Dynamic Models”, STATICS: Textbooks and Monographs, Volume 94, Marcel Dekker, Inc., New York. (1988). 20. J. E. Mottershead and M. I. Friswell,“Modal Updating in Structural Dynamics: a Survey.”, Journal of Sound and Vibration, 167(2), pp. 347-37. (1993). 21. A.K. Pandey, M. Biswas and M. M. Samman, “Damage Detection from Changes in Curvature Mode Shapes.”, Journal of Sound and Vibration, 145(2), pp. 321-332. (1991). 22. A. Rytter, “Vibration based inspection of civil engineering structures”, PhD Dissertation, Department of Building Technology and Structural Engineering, Aalborg University, Denmark. (1993). 23. D.C. Zimmerman and T. Simmermacher,“Model Correlation Using Multiple Static Load and Vibration Tests”, AIAA Journal, 33(11), pp. 2182-2188.(1995). 24. J. K. Vandiver,”Detection of Structural Failure on Fixed Platforms by Measurement of Dynamic Response”, Journal of Petroleum Technology, March, pp. 305-310, (1977). | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41993 | - |
dc.description.abstract | 有別於傳統的結構物健康診斷方法,皆須耗費許多時間與人力進行診斷結果分析與判斷。本研究主要目的,著眼於為未來開發的新一代結構物健康診斷方法,進行診斷理論方法初步的可行性驗證。以期本研究能成功適用於實際結構物健康診斷中,並以統計理論的機率運算為基礎簡易呈現最有可能之結構物破壞類別,免除需大量人力與時間投入分析與判斷。本研究乃是以結構物日常條件下微震動反應的量測紀錄為診斷原始資料,同時採用AR-ARX自迴歸時間序列作為結構物破壞情狀表徵之『結構物特徵時間序列』,運算理論則是引進生物資訊領域中應用於診斷細胞癌症基因的『貝氏分類診斷邏輯方法』作為結構物破壞類別的分類工具。
『貝氏分類診斷邏輯方法』即所謂圖像識別概念之結構物健康診斷方法,藉由瞭解已知結構物破壞情況的資料樣本係數機率密度分佈函數模型,推斷當前處於未知破壞情況條件下量測的結構物動力反應記錄最有可能的損傷程度。本研究診斷系統的理論驗證方法,以重複樣本空間與非重複樣本空間兩種比對手段,前者由於比對與被比對樣本空間是完全相同,故可作為檢驗『結構物特徵時間序列』在不同的破壞種類是否具差異性,亦即序列資料對該破壞情況具代表性;後者由於比對與被比對樣本空間是完全相異,故可模擬本研究結構健康診斷方法於實際應用上的診斷情況。而驗證步驟,則是最初以有限元素程式模擬結構物發生破壞的情況,再輸出分析得到的結構數值反應資料進行健康診斷分析。緊接著,著手進行結構物夜間微震動與震動台實驗,以真實量測得到的結構反應資料再次進行健康診斷分析,藉此瞭解現實環境中雜訊或其他不可預期因素對本研究理論方法診斷結果的影響程度。最後階段則是以貝氏分類法的最佳化理論,輔以兩階段聯集每一筆資料的診斷結果,藉以提升本研究方法的診斷準確性。 | zh_TW |
dc.description.abstract | For the research interest on this thesis, preliminary feasibility study on the application of the purposed Structure Health Monitoring (SHM) diagnosis algorithm on detecting structural damage was carried out. The fundamental theory of this applied SHM diagnosis algorithm, Bayesian Classification, was developed based on the mathematical theory of Probability and Statistics. Hence, the diagnosis results can be indicated as what sort of damage level the structure most likely suffered instead of pointing out the real damage symptoms or reasons.
This purposed SHM diagonal prototype system involves the contents including the structural health presentation techniques of AR-ARX Time Series, DNA Array comparison concept originated from bioinformation science, and the mainly integrated and computational diagnosis theory of the purposed system, the Bayesian Classification Algorithm, which is commonly used to deal with the data stream classification or digital image pattern recognition problems on the region of computer and information science. Accordingly, the integrated diagnosis procedure of this developed algorithm theory can be applied in the future to providing the near real time SHM automatically with only one electronic microprocessor set installed in-situ. Moreover, the principle diagnosis philosophy of Bayesian Classification Theory is totally relied on the comparison procedure of the measured structural intact and damaged response data in AR-ARX time series format under the day night ambient vibration condition, then the evaluation report will finally come out by choosing the one which is representing the most probably damage situation of the existing structure and simultaneously which is equivalent meaning to corresponding to the highest damage occurrence probability score among several presumed damage cases. Hereafter on this thesis, the phrase of pattern recognition SHM diagnosis is named after the property of comparing intact and damage AR-ARX structural response time series through the Bayesian Classification Algorithm. For the research methodology of theory feasibility, two examination processes – Independent and Dependent Sample Classification – and three verification stages – numerical simulation, experiment verification, union optimization – must to be checked in order to prove whether the Bayesian diagnosis algorithm can successfully detect the exact damage situation of existing structures. On the other hand, conducting the examination processes of independent and dependent sample classification on every stage can primarily, respectively, verify the theory feasibility of algorithm and the significance or discrimination degree of collected data for each damage case. Moreover, as the three verification stages arranged is aimed to gradually include the uncertainty of environment effects involved as well as to optimize the structural health diagnosis precision and accuracy by means of likelihood selection and union optimization concept. At the end, the contribution of this thesis can provide sufficient sound evidences to prove that this purposed SHM system is not only theoretical feasibility on detecting the existing structural damage situation under the day night ambient condition, but also providing the new SHM alternatives with high diagnosis precision and accuracy, otherwise, which can totally eliminate the labor effort and objective human judgment involved at analysis step unlike the traditional methods. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T00:40:58Z (GMT). No. of bitstreams: 1 ntu-97-R94521228-1.pdf: 9041820 bytes, checksum: 45d9aa982df5d23924c2044df5092fdc (MD5) Previous issue date: 2008 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii Abstract iv 第一章 緒論 1 1.1 前言 1 1.2 健康診斷方法回顧 1 1.3 研究動機與目的 4 1.4 研究內容與架構 6 第二章 貝氏分類診斷邏輯理論 8 2.1 前言 8 2.2 AR-ARX自迴歸時間序列 10 2.3 貝氏分類診斷邏輯理論 ( Naive Bayes Classification) 13 2.4 貝氏分類最佳化法則 ( Likelihood Selection ) 23 2.5 診斷系統驗證方法 27 第三章 結構物健康診斷系統之數值模擬 30 3.1 前言 30 3.2 數值模擬之分析步驟 30 3.3 結構物有限元素模型建立 31 3.3.1 模型假設 32 3.3.2 動力分析方法 33 3.4 結構物健康診斷分類結果 33 3.4.1 重複樣本空間分類 34 3.4.2 非重複樣本空間分類 37 3.5 小結 40 第四章 實驗資料庫建立 42 4.1 前言 42 4.2 結構物破壞之定義 43 4.3 資料庫種類 44 4.4 實驗規劃 46 4.5 實驗構架 48 4.6 試驗裝置 50 第五章 結構物健康診斷系統之實驗驗證 51 5.1 前言 51 5.2 研究方法 51 5.3診斷流程說明 52 5.4夜間微震動實驗之結構健康診斷系統驗證 55 5.4.1 重複樣本空間分類 56 5.4.2 非重複樣本空間分類 60 5.4.3 貝氏分類最佳化結果 69 5.4.4 兩階段聯集概念之系統健康診斷成果 76 5.4.5 訓練樣本中不含測試樣本之診斷分類 78 5.6震動台白噪音實驗對結構物健康診斷系統之干擾特性討論 82 5.6.1 重複樣本空間分類 83 5.6.2 非重複樣本空間分類 86 5.7 小結 90 第六章 結論與展望 93 6.1 研究結論 93 6.2 未來展望 95 參考文獻 97 附錄A – Sohn兩階段自迴歸時間序列理論 99 附錄B – AR與ARX自迴歸時間序列理論 108 | |
dc.language.iso | zh-TW | |
dc.title | 自迴歸時間序列與貝氏分類法於結構物健康診斷之應用 | zh_TW |
dc.title | Structural Health Monitoring by AR-ARX Time Series Analysis with Bayesian Classification | en |
dc.type | Thesis | |
dc.date.schoolyear | 96-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 羅俊雄(Chin-Hsiung Loh),謝尚賢(Shang-Hsien Hsieh),林子剛(Tzu-Kun Lin) | |
dc.subject.keyword | 結構物健康診斷,AR-ARX結構物特徵時間序列,貝氏機率理論, | zh_TW |
dc.subject.keyword | Structural Health Monitoring,AR-ARX Time Series,Bayesian Theory, | en |
dc.relation.page | 117 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2009-12-24 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-97-1.pdf 目前未授權公開取用 | 8.83 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。