本文研究係針對系統受非定常激勵，利用基於協方差分析的唯輸出系統識別法進行結構模態參數識別。前人已證明資料相關特徵系統實現法(Eigensystem Realization Algorithm using Data Correlation, ERA/DC)與協方差型隨機子空間識別法(Covariance-driven Stochastic Subspace Identification, SSI-COV)皆可直接利用系統之響應信號進行系統參數識別，但僅適用於激勵信號為定常白雜訊的假設條件。然而實際環境振動大部分屬於非定常訊號，即訊號統計值會隨時間改變，因此並不完全與白雜訊的假設相符。本文延伸此概念，探討線性結構系統於非定常過程環境激勵下，如何有效的進行模態參數識別。本文針對具有乘積模型(product model)之非定常響應，發展一唯輸出模態估測理論；藉由建立系統非定常響應之協方差矩陣，進而去除非定常激勵或雜訊對系統造成的影響，並結合ERA/DC與SSI-COV，從而將適用性推廣至非定常分析。經數值模擬與實驗驗證結果顯示，在適當的非定常環境振動情況下，本文所提出之改良方法可獲得良好的模態參數估測結果，且可有效估測具模態干涉系統之結構模態。

This thesis aimed to explore modal estimation by using the output-only identification method based on covariance analysis from response of structural system subjected to nonstationary excitation. The previous studies have shown that Eigensystem Realization Algorithm using Data Correlation (ERA/DC) and Covariance-driven Stochastic Subspace Identification (SSI-COV) methods are applicable to perform response-only modal estimation, but only for the assumption of the excitation to be stationary white noise. However, most ambient vibrations belong to nonstationary process with time varying statistics. This study extended this concept and explored how to implement modal identification of a linear structural system subjected to nonstationary excitation. In this thesis, we develop a complete theory based on ERA/DC and SSI-COV for the nonstationary process in the form of a product model. Numerical simulations and experimental verifications validated the effectiveness of the proposed method, and further explored the effectiveness of modal estimation of a system with modal interference.

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