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Time Series Analysis for Vibration-Based Structural Health Monitoring: A Review

Kong Fah Tee 1,*
School of Engineering, University of Greenwich, Kent, UK.
*Corresponding Author: Kong Fah Tee. Email: .

Structural Durability & Health Monitoring 2018, 12(3), 129-147. https://doi.org/ 10.3970/sdhm.2018.04316

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.

Keywords

Time series snalysis, structural health monitoring, structural damage detection, autoregressive model, damage sensitive features.

Cite This Article

Tee, K. F. (2018). Time Series Analysis for Vibration-Based Structural Health Monitoring: A Review. Structural Durability & Health Monitoring, 12(3), 129–147.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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