Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74033
Title: Time synchronization for acceleration measurement data of Jiangyin Bridge subjected to a ship collision
Authors: Wang, WD
Jiang, SF
Zhou, HF
Yang, M
Ni, YQ 
Ko, JM 
Keywords: Acceleration
Ship collision
State-space model
Structural health monitoring
Time synchronization
Issue Date: 2018
Publisher: John Wiley & Sons
Source: Structural control and health monitoring, 2018, v. 25, no. 1, e2039 How to cite?
Journal: Structural control and health monitoring 
Abstract: State-space (SS) model is proposed to identify the time lag between the asynchronous accelerations at different locations of the Jiangyin Bridge measured during a ship-bridge collision. One of the accelerations is chosen as the reference signal, and the time axis of the rest of them are shifted relative to that of the reference signal with a series of shifting times. For each pair of reference and time shifted signals, SS models in correspondence to the shifting time series are formulated. Their system matrices are identified with data-driven stochastic subspace identification algorithm, and their model order is determined by Akaike's information theoretic criterion and final prediction error. If the 2 accelerations for model fitting are asynchronous, errors may be introduced into the SS model and its prediction error is expected to be greater than the counterpart obtained with synchronous accelerations. Therefore, the actual time lag between them is identified from the shifting time that corresponds to the minimum of loss function, which is used to map the prediction error vector sequence to a real number. In addition, asynchronous acceleration data measured 2 hr ahead of the ship-bridge collision and synchronous acceleration data measured long after the ship-bridge collision are also analyzed. The former dataset is exploited to evaluate the reproducibility of the SS model for time synchronization, and the latter dataset is utilized to examine its anti-false-identification capability. The results show that the SS model achieves a satisfactory performance in the identification of time lag for both asynchronous and synchronous measurement data.
URI: http://hdl.handle.net/10397/74033
ISSN: 1545-2255
EISSN: 1545-2263
DOI: 10.1002/stc.2039
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

2
Last Week
0
Last month
Citations as of Mar 29, 2019

Page view(s)

46
Last Week
0
Last month
Citations as of May 21, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.