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研究生:黃罡
研究生(外文):Huang, Gang
論文名稱:應用無線監測技術之唯輸出系統結構損傷探測
論文名稱(外文):Application of Wireless Sensing Technology for Damage Detection of Output-Only Structural Systems
指導教授:王彥博
指導教授(外文):Wang, Yen-Po
學位類別:碩士
校院名稱:國立交通大學
系所名稱:土木工程學系
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:232
中文關鍵詞:唯輸出隨機子空間識別系統識別多變量分析結構損傷探測無線感測
外文關鍵詞:output-onlystochastic subspace identificationsystem identificationmultivariate analysisstructural damage detectionwireless sensing
相關次數:
  • 被引用被引用:12
  • 點閱點閱:282
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  • 下載下載:19
  • 收藏至我的研究室書目清單書目收藏:1
本研究結合隨機子空間系統識別法(Stochastic SubspaceI dentification,SSI))與狀態空間DLV損傷定位法,針對唯輸出系統之地震監測反應進行結構損傷定位。經由數值模擬分析及一系列之振動台地震模擬試驗,證明吾人可在未知輸入擾動資訊的條件下,透過結構全域之地震反應歷時定位出平面構架結構之局部受損位置﹙樓層﹚。本研究採用SSI-COV以及結合多變量分析之SSI-PLS、SSI-MLR、SSI-CCA及SSI-ECCA等在隨機子空間系統架構下所發展之系統識別分析方法,其概念為透過輸出向量序列間之協方差關係,建立包含原變量特性之降階系統,減少矩陣維度過高對精確性之影響。數值模擬分析結果顯示,結合多變量分析之SSI識別法,由於引進了卡爾曼過濾器之觀念,因此無論對於損傷程度之敏感性或是噪音干擾之適應性,皆較SSI-COV識別法為佳。振動台試驗採用無線監測技術,以台灣大學所發展之無線傳輸系統NTU-WSU結合加速規進行動態反應監測。試驗結果顯示,包含SSI-COV在內的五種系統識別法結合DLV損傷探測法多能有效定位出受損樓層,其中以SSI-ECCA之結果最佳,在單一樓層受損及複數樓層受損案例中皆能將所有受損樓層定位出,證明其於實例應用上之可行性。惟五種方法都會發生將未受損之ㄧ樓誤判的情況,主要是輸入擾動乃不具穩態特性之隨機過程(白噪音案例亦因振動台性能之限制未能忠實呈現高斯訊號的特性)所致。
In this study, a scheme integrated with the stochastic subspace system identification (SSI) and state-space damage localization (DLV) method has been developed for structural damage detection of output-only systems. A series of numerical simulations and shaking table tests has been conducted. Encouraging results indicate that local (storey) damages of plan frames can be identified from global seismic response data without knowledge of the input disturbance. In this study, the SSI-COV as well as SSI-PLS, SSI-MLR, SSI-CCA, and SSI-ECCA techniques developed for system identification under the framework of stochastic state-space system via the multivariate analysis are considered. The concept of multivariate analysis is to identify system parameters of the dynamically equivalent reduced-order system from the covariance matrix consisting of the output state vector sequences in a way that adverse effects on the accuracy of oversized data set can be eliminated. Simulation results indicate that, with the introduction of Kalman filter, the multivariate-analysis-based techniques are more sensitive to moderate structural damages and adaptive to noise than the SSI-COV approach. The wireless sensing technique has been adopted for dynamic response monitoring in the shaking table tests using the wireless communication device, NTU-WSU, developed by NTU, in connection with accelerometers. Results show that the damaged stories of the output-only systems can in general be effectively located with the DLV damage detection method using the system parameters identified by all the methods, among which the SSI-ECCA proves to be the most effective one. With the SSI-ECCA, all damage stories can be identified regardless of single or multiple-storey damage conditions, indicating great potential of practical applications. Nevertheless, in all cases the first storey has been judged as potentially damaged, whether or not it is really damaged. This might be attributed to the non-Gaussian characteristics of the input excitations The white noise processes adopted in the shaking table tests system does not reflect the desired properties of Gaussian due to performance limitations of the hydraulic actuator.
第一章 緒論 1
1.1研究動機與目的 1
1.2文獻回顧 2
1.3論文架構 7
第二章 SSI系統識別分析理論 9
2.1前言 9
2.2 隨機預測子空間系統識別(SSI)理論回顧 10
2.2.1狀態空間方程式 11
2.2.2 隨機預測狀態方程式(Stochastic State-Space System) 13
2.2.3 隨機子空間系統識別-協方差法(SSI-COV) 13
2.2.3.1隨機狀態系統 13
2.2.3.2隨機狀態空間系統之參數識別 15
2.3 卡爾曼濾波模型( The Kalman filter model) 18
2.4 多變量分析(Multivariate Analysis) 22
2.4.1 多變量統計分析之概念 23
2.4.2 局部最小平方法(Partial Least Squares , PLS) 25
2.4.3 典型相關分析法(Canonical Correlation Analysis, CCA) 28
2.4.4 多元迴歸分析法(Multiple Linear Regression , MLR) 31
2.5 卡爾曼狀態向量之多變量分析 36
2.5.1 卡爾曼輸出向量預估 36
2.5.2透過局部最小平方法(PLS)建立卡爾曼狀態向量 38
2.5.3透過典型相關分析法(CCA)建立卡爾曼狀態向量 39
2.5.4透過多元迴歸分析法(MLR)建立卡爾曼狀態向量 41
2.5.5改良典型相關分析(Enhanced CCA,簡稱ECCA) 42
2.6隨機狀態空間系統之參數識別 44
2.7萃取系統模態參數 50
2.8數值範例 51
第三章 DLV損傷探測分析之理論與數值驗證 61
3.1前言 61
3.2 DLV損傷探測理論 62
3.2.1 DLV法之理論回顧 62
3.2.2 狀態空間DLV法之理論回顧 65
3.3結合SSI系統識別之損傷探測分析 70
第四章 狀態空間DLV法之數值模擬驗證 73
4.1前言 73
4.2五層樓剪力屋架之結構損傷探測 73
4.2.1單一樓層受損情況之診斷分析 74
4.2.2複數樓層受損情況之診斷分析 78
4.3結構損傷探測於噪訊中之分析 80
4.3.1單一樓層受損情況之診斷分析 81
4.3.2複數樓層受損情況之診斷分析 86
4.4結構損傷探測之敏感度分析 89
4.3.1單一樓層受損情況之診斷分析 89
4.3.2複數樓層受損情況之診斷分析 97
4.4 小結 101
第五章 無線監測技術於結構損傷探測試驗驗證 165
5.1前言 165
5.2試驗設備 165
5.2.1無線感應器與原有線感應器之比較 168
5.3 DLV損傷探測分析 169
5.4 小結 177
第六章 結論與建議 225
參考文獻 229
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