Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion
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Version 1 2015-03-06, 09:51Version 1 2015-03-06, 09:51
journal contribution
posted on 2024-06-05, 10:58 authored by AM Ay, Y WangStatistical 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 MonitoringVolume
13Pagination
445-460Location
England, LondonPublisher DOI
ISSN
1475-9217eISSN
1741-3168Language
engPublication classification
C Journal article, C1 Refereed article in a scholarly journalCopyright notice
2014, SAGE PublicationsIssue
4Publisher
SAGE Publications LtdUsage metrics
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No categories selectedKeywords
auto-regressive moving average with exogenous input modelDamage identificationmulti-sensor data fusionself-fittingsteel frameScience & TechnologyTechnologyEngineering, MultidisciplinaryInstruments & InstrumentationEngineeringVIBRATION DATALOCALIZATION090506 Structural Engineering090609 Signal Processing970101 Expanding Knowledge in the Mathematical SciencesSchool of Engineering
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