臺灣博碩士論文加值系統

本研究主要目的在使用隨機子空間鑑別法，鑑別結構中的接點參數，有別於一般頻率域的方法，使用時域的方法，減去人工給予結構物衝擊的操作，提升鑑別品質。
隨機子空間鑑別法是利用環境所造成的各種微小震動，當做鑑別時，給予結構物的震動，以求得結構物的反應值，再進行參數分析。隨機子空間鑑別法使用了相關性、投影、奇異值分解等方法。
使用MATLAB模擬時發現，只要了解待測結構物的特性，對程式進行適當的調整，隨機子空間鑑別法鑑別後的結果不差，誤差都在百分之5之下，且僅需使用加速規所測得的訊號就可鑑別。

This research aims at estimation of strueture using the data driven stochastic subspace identification. Differ from frequency domain method . The time domain method can resuce the people give an input for structural and promotethe efficiency of the identification processes.
The data driven stochastic subspace identification is use the noise by environment and use those noise to arouse structural to get the output to do analysis. Stochastic subspace identification use the covariance and projection and singular value decomposition.
Use the MATLAB to simulate, the result erro margin not over five percent, if understand the structural characteristic and to adjust the program, and only ues the accelerometer to identification.

中文摘要..............................................vi
英文摘要.............................................vii
致謝................................................viii
目錄..................................................ix
表目錄................................................xi
圖目錄...............................................xii
符號表..............................................xiii
第一章　　緒論..........................................1
1.1　　　研究動機.......................................1
1.2　　　文獻回顧.......................................5
1.3　　　研究目標.......................................7
1.4　　　本文大綱.......................................8
第二章　　理論推導......................................9
2.1　　　State Sapace Model............................9
2.1.1　 離散時間的State-Space Models..................11
2.2　　　馬可夫參數....................................13
2.3　　　SSI-COV理論架構...............................14
2.3.1　 隨機過程之基本假設.............................14
2.3.2　 量測資料重組...................................15
2.3.3　 SSI-COV識別法.................................16
2.4　　 SSI-DATA理論架構...............................18
2.4.1　 由輸出向量重建系統狀態..........................18
2.4.2　 投影概念......................................19
2.5　　　提出系統模態數.................................21
2.6　　　討論..........................................23
2.6.1　 SSI-COV之特性..................................23
2.6.2　 SSI-DATA之特性.................................23
第三章　 數值模擬.......................................26
3.1　　　驗證隨機子空間鑑別法之可行性.....................27
3.1.1　 各種雜訊之鑑別結果..............................27
3.2　　 模擬結論.......................................32
第四章　 結論..........................................46
參考文獻...............................................48
簡歷...................................................52

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