Automatic (operational) modal analysis for Stochastic Subspace Identification
Date Issued
2015
Date
2015
Author(s)
Hsu, Wei-Ting
Abstract
Operational modal analysis has been proven to be an efficient tool for the identification of liner-time-invariant system using multivariate measurements. In particular, Stochastic Subspace Identification (SSI) is one of the powerful algorithms in structural health monitoring (SHM). Generally, the estimated modal parameters through SSI may be afflicted with statistical uncertainty, e.g. undefined measurement noises, non-stationary excitation, environmental condition, finite number of data samples, and etc. Therefore, the identified results are subjected to variance errors. Accordingly, the concept of the system stabilization diagram can help users to identify the correct modes, i.e. through physical criteria to remove the spurious modes. Modal parameters can be estimated at successive model orders where the physical modes of the system are extracted and separated from the spurious modes. Another issue has been raised on the subjective judgement of selecting the pre-defined parameters, i.e. the modal orders, row length of data Hankel matrix and threshold values of criteria on stabilization diagram. To avoid relying on engineer judgment when conducting SSI, an automated SSI algorithm is developed and discussed in this thesis. First of all, the identification of modal parameters through covariance-driven stochastic subspace identification (SSI-COV) from the output-only measurements is applied with the automated scheme. A systematic way of investigation on the criteria for the stabilization diagram is presented. Secondly, a statistical approach is utilized to separate physical modes from spurious modes. Finally, the computation of uncertainty bounds for each mode with all model order in the stabilization diagram is presented to determine system natural frequencies and damping ratios. Demonstration of this study on the system identification of: (1) a three-span steel bridge under operation condition, (2) an experimental bridge scouring test and (3) a 3-story steel frame under a series of shaking table tests are presented. Each case study represented a different condition of system including: (1) a time-invariant system, (2) a time-variant system and (3) a nonlinear system, respectively. Several system identification tools such as the data-driven Subspace Identification (SI-DATA) and Frequency Domain Decomposition (FDD), are also applied to help users to recognize the results of the proposed algorithm. Moreover, further assessment of structural damage severity can be proceeded through damage detection methods. All in all, it is shown that the proposed new operation procedure for the automated covariance-driven stochastic subspace identification can enhance the robustness and reliability in structural health monitoring.
Subjects
Stochastic Subspace Identification
stabilization diagram
clustering
uncertainty bounds
structural health monitoring
damage detection
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-104-R02521247-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):e2e067c37c56a035c78bdb4fcde24fc3