以往環境模態分析因環境激勵的隨機性，會假設激勵為定常白訊，然而實際環境不一定為白訊，意即環境激勵中某些頻率的訊號能量較高。根據前人研究可利用定常白訊經由一假想系統可獲得定常非白訊之響應，再利用此響應作用於待識別之系統即可模擬在定常非白訊之環境下系統之動態行為。吾人利用時域法之資料型隨機子空間識別法探討定常非白訊之環境振動問題，資料型隨機子空間識別法是利用奇異值分解判別系統階數後再進一步計算模態參數。由數值分析顯示，對於激勵定常非白訊的參數識別，在判別系統階數時要同時考慮待識別系統之階數與假想系統之階數，藉此才有足夠之動態資訊以利識別。

In operational modal analysis, we usually assume that excitation is a stationary white noise for ambient randomness. According to previous studies, a non-white response can be obtained through a hypothetical system using stationary white noise. Furthermore, the response can be applied to the system to simulate the dynamic behavior of the system in a non-white ambient vibration. In the research, we solve the problems of modal identifi-cation for non-white ambient vibration by Data-driven Stochastic Subspace Identifica-tion (SSI-DATA). SSI-DATA determines the orders of the system by singular value de-composition and calculates the modal parameters. For the modal parameter identification of system in ambient vibration of non-white noise, the numerical analysis shows that the order of the identified system and the order of the hypothetical system must be taken into consideration when determining the system order, so that we get correct identifica-tions with sufficient dynamic information.

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