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Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion

Version 2 2024-06-05, 10:58
Version 1 2015-03-06, 09:51
journal contribution
posted on 2024-06-05, 10:58 authored by AM Ay, Y Wang
Statistical time series methods have proven to be a promising technique in structural health monitoring, since it provides a direct form of data analysis and eliminates the requirement for domain transformation. Latest research in structural health monitoring presents a number of statistical models that have been successfully used to construct quantified models of vibration response signals. Although a majority of these studies present viable results, the aspects of practical implementation, statistical model construction and decision-making procedures are often vaguely defined or omitted from presented work. In this article, a comprehensive methodology is developed, which essentially utilizes an auto-regressive moving average with exogenous input model to create quantified model estimates of experimentally acquired response signals. An iterative self-fitting algorithm is proposed to construct and fit the auto-regressive moving average with exogenous input model, which is capable of integrally finding an optimum set of auto-regressive moving average with exogenous input model parameters. After creating a dataset of quantified response signals, an unlabelled response signal can be identified according to a 'closest-fit' available in the dataset. A unique averaging method is proposed and implemented for multi-sensor data fusion to decrease the margin of error with sensors, thus increasing the reliability of global damage identification. To demonstrate the effectiveness of the developed methodology, a steel frame structure subjected to various bolt-connection damage scenarios is tested. Damage identification results from the experimental study suggest that the proposed methodology can be employed as an efficient and functional damage identification tool. © The Author(s) 2014.

History

Journal

Structural Health Monitoring

Volume

13

Pagination

445-460

Location

England, London

ISSN

1475-9217

eISSN

1741-3168

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2014, SAGE Publications

Issue

4

Publisher

SAGE Publications Ltd