Statistical model-based damage detection and localization: subspace-based residuals and damage-to-noise sensitivity ratios

Basseville, Michèle; Mevel, Laurent; Goursat, Maurice

doi:10.1016/j.jsv.2003.07.016

Cited by 129 publications

(137 citation statements)

References 32 publications

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“…For the damage detection method in [2,3] a residual function is constructed based on properties from covariance-driven outputonly subspace-based system identification [13,10].…”

Section: Properties From Subspace-based System Identificationmentioning

confidence: 99%

“…In the following, the non-parametric damage detection test [1] based on [2,3] is described. Using measured data (y k ) k=1,...,N , a consistent estimate H p+1,q of the Hankel matrix is obtained from the empirical output covariances…”

Section: The Damage Detection Testmentioning

confidence: 99%

“…is defined [2,3]. It is tested if this residual is significantly different from zero or not, using the χ 2 test…”

Section: The Damage Detection Testmentioning

confidence: 99%

“…In this paper we consider the subspace-based damage detection approach developed in [2,3,5,6]. With this approach, a data-driven model obtained in the reference state is compared to data from the possibly damaged state using a subspacebased residual function and a χ 2 -test built on it for a hypothesis test, without actually estimating the modal parameters in the tested, possibly damaged states.…”

Section: Introductionmentioning

confidence: 99%

“…Since this kind of methods process the measurement data in a much more direct way, the original algorithms from [2,3] have the drawback of being influenced by changes in the unknown ambient excitation, which may lead to false alarms. Recently, the original algorithms have been extended and modified to be robust under such changes in the excitation in [6].…”

Section: Introductionmentioning

confidence: 99%

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Subspace-Based Damage Detection on Steel Frame Structure Under Changing Excitation

Döhler

Hille

2014

Conference Proceedings of the Society for Experimental Mechanics Series

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To cite this version:Michael Döhler, Falk Hille. Subspace-based damage detection on steel frame structure under changing excitation. AbstractDamage detection can be performed by detecting changes in the modal parameters between a reference state and the current (possibly damaged) state of a structure from measured output-only vibration data. Alternatively, a subspace-based damage detection test has been proposed and applied successfully, where changes in the modal parameters are detected, but the estimation of the modal parameters themselves is avoided. Like this, the test can run in an automated way directly on the vibration measurements. However, it was assumed that the unmeasured ambient excitation properties during measurements of the structure in the reference and possibly damaged condition stay constant, which is hardly satisfied by any application. A new version of the test has been derived recently that is robust to such changes in the ambient excitation. In this paper, the robust test is recalled and its performance is evaluated both on numerical simulations and a real application, where a steel frame structure is artificially damaged in the lab.

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“…For the damage detection method in [2,3] a residual function is constructed based on properties from covariance-driven outputonly subspace-based system identification [13,10].…”

Section: Properties From Subspace-based System Identificationmentioning

confidence: 99%

Section: The Damage Detection Testmentioning

confidence: 99%

“…is defined [2,3]. It is tested if this residual is significantly different from zero or not, using the χ 2 test…”

Section: The Damage Detection Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Subspace-Based Damage Detection on Steel Frame Structure Under Changing Excitation

Döhler

Hille

2014

Conference Proceedings of the Society for Experimental Mechanics Series

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Model‐Based Statistical Signal Processing for Change and Damage Detection

Basseville

2008

Encyclopedia of Structural Health Monitoring

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Handling parameterized models for monitoring structures or processes is a natural approach to damage or fault detection and diagnosis. Modeling damages and faults as deviations in the parameter vector with respect to its nominal value in safe conditions calls for the use of statistical change detection and isolation methods. Such methods share the ability of handling noises and uncertainties with other statistical approaches. The purpose of this article is to describe the key elements in the design of change detection algorithms. Basic concepts (likelihood ratio, estimation, and innovation) are introduced. These tools involve a number of familiar operations (integration, averaging, sensitivity, adaptive thresholds, and windows). It is suggested that a statistical framework enlightens the meaning and increases the power of those operations. Vibration‐based structural health monitoring is addressed within this framework.

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Usage Management of Civil Structures

Miyamoto

2008

Encyclopedia of Structural Health Monitoring

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Bridge health monitoring using information technology and sensors is capable of providing more accurate knowledge of bridge performance than traditional strategies. This paper introduces a state of the art on not only an IT‐based bridge health monitoring system incorporated with the latest information technologies for lifetime management of existing bridges but also a data‐collecting system designed for bridge health monitoring.

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Statistical model-based damage detection and localization: subspace-based residuals and damage-to-noise sensitivity ratios

Cited by 129 publications

References 32 publications

Subspace-Based Damage Detection on Steel Frame Structure Under Changing Excitation

Subspace-Based Damage Detection on Steel Frame Structure Under Changing Excitation

Model‐Based Statistical Signal Processing for Change and Damage Detection

Usage Management of Civil Structures

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