本文探討非定常時間序列於非定常環境振動模態參數識別之應用。原時間序列法使用定常ARMA模型來描寫定常響應訊號，並提取模型自迴歸項參數，進而獲取系統之模態參數。然而面對更貼近真實環境的非定常訊號，則通常使用在定常訊號的時間序列便不再適用，因此本文分別以振幅曲線擬合及引入基底函數的方式，加以描述非定常訊號其振幅非定常的因素，建構非定常時間序列模型，使其能良好描寫非定常訊號，並應用於非定常白訊激勵之結構系統模態參數識別，進而得到系統之模態參數。經由數值模擬結果顯示，在非定常環境振動情況下本文所提之分析法可得良好的模態參數識別結果。

This thesis studies Non-Stationary Time Series for the application of modal-parameter identification from non-stationary ambient vibration data. The original Time Series uses ARMA (Autoregressive Moving-Average) model, which contains autoregressive part and moving average part, to reconstruct the stationary ambient vibration data, and obtains modal parameter with autoregressive part of ARMA model. However, the original time series method is not applicable to non-stationary signal which is closer to natural environment. So we propose two ways to build a non-stationary time series model—by curve-fitting of amplitude and by introducing the basis function. We use this model to describe the non-stationary amplitude of data and we also apply it to modal-parameter identification from non-stationary ambient vibration data. Through numerical simulation, applicability and effectiveness of the proposed method of modal parameter identification from non-stationary ambient vibration data is demonstrated.

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