An extended time series algorithm for modal identification from nonstationary ambient response data only

Chang Sheng Lin, Dar Yun Chiang, Tse Chuan Tseng

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Modal Identification is considered from response data of structural systems under nonstationary ambient vibration. The conventional autoregressive moving average (ARMA) algorithm is applicable to perform modal identification, however, only for stationary-process vibration. The ergodicity postulate which has been conventionally employed for stationary processes is no longer valid in the case of nonstationary analysis. The objective of this paper is therefore to develop modal-identification techniques based on the nonstationary time series for linear systems subjected to nonstationary ambient excitation. Nonstationary ARMA model with time-varying parameters is considered because of its capability of resolving general nonstationary problems. The parameters of moving averaging (MA) model in the nonstationary time-series algorithm are treated as functions of time and may be represented by a linear combination of base functions and therefore can be used to solve the identification problem of time-varying parameters. Numerical simulations confirm the validity of the proposed modal-identification method from nonstationary ambient response data.

Original languageEnglish
Article number391815
JournalMathematical Problems in Engineering
Volume2014
DOIs
Publication statusPublished - 2014 Jan 1

All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Engineering(all)

Fingerprint Dive into the research topics of 'An extended time series algorithm for modal identification from nonstationary ambient response data only'. Together they form a unique fingerprint.

Cite this