A cointegration-based approach for automatic anomalies detection in large-scale structures

Turrisi, Simone; Cigada, Alfredo; Zappa, Emanuele

doi:10.1016/j.ymssp.2021.108483

Cited by 19 publications

(8 citation statements)

References 35 publications

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“…Over the past decade, cointegration analysis for endogenous variables has been increasingly used for structural damage detection. [19][20][21][22][23] The key points are briefly recapitulated in this section to help understand the proposed cointegration-based damage localization method.…”

Section: Cointegration Analysis For Endogenous Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The effectiveness of using cointegration to distinguish the changes in the DSFs caused by environmental variations from those related to structural damage has been demonstrated in the literature. [19][20][21][22][23] In this article, cointegration analysis with exogenous variables will be employed to eliminate the effects of nonstationary excitations, thereby developing an output-only damage localization method. Although the dynamic responses of a multi-segment riser are usually nonstationary, those within an individual pipe segment often have synchronous fluctuations with a common trend, allowing for the application of a multivariate time series analysis.…”

Section: Introductionmentioning

confidence: 99%

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Output-only structural damage localization for deepsea mining risers using cointegration analysis

Wang

et al. 2022

Structural Health Monitoring

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Deepsea mining risers, which transport mineral resources from the seafloor to surface facilities, are critical components of deepsea mining systems. This article presents a novel cointegration-based method for localizing structural damage in risers subjected to random excitations from waves, currents, and moving floating facilities. Although the dynamic responses of a riser (usually consisting of multiple segments) are often nonstationary, those within an individual pipe segment often have synchronous fluctuations with a common trend, allowing the application of a multivariate time series analysis. Cointegration can thus be performed for a pipe segment by treating the internal responses as the endogenous variables and those of the other pipe segments as the exogenous variables. Consequently, a stationary cointegrating residual for the dynamic responses purged of the influence of nonstationary excitations can be obtained. The cointegrating residual in conjunction with a statistical hypothesis test scheme is used to create an output-only damage indicator. The effectiveness of the proposed damage localization method is demonstrated with a numerical and experimental multi-segment riser. The results indicate that the strain responses of both risers exhibit nonstationary fluctuations due to the changeable environmental excitations. As the proposed method is robust to the external loading, it can accurately identify the damage location.

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Section: Cointegration Analysis For Endogenous Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Output-only structural damage localization for deepsea mining risers using cointegration analysis

Wang

et al. 2022

Structural Health Monitoring

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“…The cointegration-based approach has been applied to SHM in the last few years as a potential data normalisation tool for the removal of long-term common trends, caused by the effects of variability in EOCs, from the measured data. Some selected applications of cointegration-based approaches for SHM can be found in [25][26][27][28][29][30][31][32][33][34][35][36][37]. Since cointegration can efficiently eliminate the impact of EOCs from the SHM data, one obtains cointegration residuals that maintain their sensitivity to damage while becoming independent of EOCs.…”

Section: Introductionmentioning

confidence: 99%

Lamb Wave-Based Structural Damage Detection: A Time Series Approach Using Cointegration

Dao

2023

Materials

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Although Lamb waves have found extensive use in structural damage detection, their practical applications remain limited. This limitation primarily arises from the intricate nature of Lamb wave propagation modes and the effect of temperature variations. Therefore, rather than directly inspecting and interpreting Lamb wave responses for insights into the structural health, this study proposes a novel approach, based on a two-step cointegration-based computation procedure, for structural damage evaluation using Lamb wave data represented as time series that exhibit some common trends. The first step involves the composition of Lamb wave series sharing a common upward (or downward) trend of temperature. In the second step, the cointegration analysis is applied for each group of Lamb wave series, which represents a certain condition of damage. So, a cointegration analysis model of Lamb wave series is created for each damage condition. The geometrical and statistical features of Lamb wave series and cointegration residual series are used for detecting and distinguishing damage conditions. These features include the shape, peak-to-peak amplitude, and variance of the series. The validity of this method is confirmed through its application to the Lamb wave data collected from both undamaged and damaged aluminium plates subjected to temperature fluctuations. The proposed approach can find its application not only in Lamb wave-based damage detection, but also in other structural health monitoring (SHM) systems where the data can be arranged in the form of sharing common environmental and/or operational trends.

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“…Into details, cointegration is first used to create a linear combination of the original measured variables

, where

is referred to as cointegration residual, in which all the effects of EOVs are deleted. Other recent successful implementations of cointegration to eliminate environmental and operational trends from damage sensitive features can be found in [ 24 , 25 , 26 , 27 , 28 ]. The expression of the cointegration residual can equivalently be reformulated as a linear relationship where a generic variable

is selected as response and the remaining variables becomes the regressors

, i.e.,

, thus enabling to predict the behaviour of

.…”

Section: Introductionmentioning

confidence: 99%

Combined Use of Cointegration Analysis and Robust Outlier Statistics to Improve Damage Detection in Real-World Structures

Turrisi

Zappa

Cigada

2022

Sensors

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Due to the need for controlling many ageing and complex structures, structural health monitoring (SHM) has become increasingly common over the past few decades. However, one of the main limitations for the implementation of continuous monitoring systems in real-world structures is the effect that benign influences, such as environmental and operational variations (EOVs), have on damage sensitive features. These fluctuations may mask malign changes caused by structural damages, resulting in false structural condition assessment. When damage identification is implemented as novelty detection due to the lack of known damage states, outliers may be part of the data set as the result of the benign and malign factors mentioned above. Thanks to the developments in the field of robust outlier detection, the current paper presents a new data fusion method based on the use of cointegration and minimum covariance determinant estimator (MCD), which allows us to visualize and to classify outliers in SHM data, depending on their origin. To validate the effectiveness of this technique, the recent case study of the KW51 bridge has been considered, whose natural frequencies are subjected to variations due to both EOVs and a real structural change.

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A cointegration-based approach for automatic anomalies detection in large-scale structures

Cited by 19 publications

References 35 publications

Output-only structural damage localization for deepsea mining risers using cointegration analysis

Output-only structural damage localization for deepsea mining risers using cointegration analysis

Lamb Wave-Based Structural Damage Detection: A Time Series Approach Using Cointegration

Combined Use of Cointegration Analysis and Robust Outlier Statistics to Improve Damage Detection in Real-World Structures

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