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Time series analysis for vibration-based structural health monitoring: A review

Time series analysis for vibration-based structural health monitoring: A review

Tee, Kong Fah ORCID: 0000-0003-3202-873X (2018) Time series analysis for vibration-based structural health monitoring: A review. Structural Durability and Health Monitoring, 12 (3). pp. 129-147. ISSN 1930-2983 (doi:https://doi.org/10.3970/sdhm.2018.04316)

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Abstract

Structural health monitoring (SHM) is a vast, interdisciplinary research field whose literature spans several decades with focusing on condition assessment of different types of structures including aerospace, mechanical and civil structures. The need for quantitative global damage detection methods that can be applied to complex structures has led to vibration-based inspection. Statistical time series methods for SHM form an important and rapidly evolving category within the broader vibration-based methods. In the literature on the structural damage detection, many time series-based methods have been proposed. When a considered time series model approximates the vibration response of a structure and model coefficients or residual error are obtained, any deviations in these coefficients or residual error can be inferred as an indication of a change or damage in the structure. Depending on the technique employed, various damage sensitive features have been proposed to capture the deviations. This paper reviews the application of time series analysis for SHM. The different types of time series analysis are described, and the basic principles are explained in detail. Then, the literature is reviewed based on how a damage sensitive feature is formed. In addition, some investigations that have attempted to modify and/or combine time series analysis with other approaches for better damage identification are presented.

Item Type: Article
Uncontrolled Keywords: Time series snalysis, structural health monitoring, structural damage detection, autoregressive model, damage sensitive features.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Last Modified: 25 Mar 2019 10:59
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/23182

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