Handling the temperature effect in vibration monitoring of civil structures: A combined subspace-based and nuisance rejection approach

Balmès, Étienne; Basseville, Michèle; Mevel, Laurent; Nasser, Houssein

doi:10.1016/j.conengprac.2008.05.010

Cited by 25 publications

(13 citation statements)

References 24 publications

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“…Observed at r sensor positions (e.g. by displacement, velocity or acceleration sensors) at discrete time instants t = kτ (with sampling rate 1/τ), system (1) can be transformed into a discrete-time state space system model [18] x k+1 = Ax k + v k y k = Cx k + w k (2) with the state vector x k = z(kτ) Tż (kτ) T T ∈ R n , the measured outputs y k ∈ R r , the state transition matrix…”

Section: Dynamic Structural System Modelmentioning

confidence: 99%

“…loss of mass or loss of stiffness, and affect the system matrices (A,C) in (2) and the modal parameters (natural frequencies, damping ratios, mode shapes). Hence, damages in model (1) can be equivalently detected as changes in the system matrices in (2). Based on measurements y k of the monitored system from a reference and from a possibly damaged state, a residual vector is defined based on subspace properties, as outlined in the following section.…”

Section: Dynamic Structural System Modelmentioning

confidence: 99%

“…In the parametric way, a model describes the influence of the temperature on the monitored structure. Then, variations in the temperature parameter can be rejected in the test statistic as nuisance or the left null space matrix S can be adjusted with the temperature [2,5].…”

Section: Robustness To Environmental Nuisancesmentioning

confidence: 99%

See 2 more Smart Citations

Vibration-Based Monitoring of Civil Structures with Subspace-Based Damage Detection

Döhler¹,

Hille²,

Mevel³

2018

Intelligent Systems, Control and Automation: Science and Engineering

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Automatic vibration-based structural health monitoring has been recognized as a useful alternative or addition to visual inspections or local non-destructive testing performed manually. It is, in particular, suitable for mechanical and aeronautical structures as well as on civil structures, including cultural heritage sites. The main challenge is to provide a robust damage diagnosis from the recorded vibration measurements, for which statistical signal processing methods are required. In this chapter, a damage detection method is presented that compares vibration measurements from the current system to a reference state in a hypothesis test, where datarelated uncertainties are taken into account. The computation of the test statistic on new measurements is straightforward and does not require a separate modal identification. The performance of the method is firstly shown on a steel frame structure in a laboratory experiment. Secondly, the application on real measurements on S101 Bridge is shown during a progressive damage test, where damage was successfully detected for different damage scenarios.

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Section: Dynamic Structural System Modelmentioning

confidence: 99%

Section: Dynamic Structural System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Vibration-Based Monitoring of Civil Structures with Subspace-Based Damage Detection

Döhler¹,

Hille²,

Mevel³

2018

Intelligent Systems, Control and Automation: Science and Engineering

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“…While sensitivity tests have been used for damage localization in (Basseville et al, 2004;Balmès et al, 2008), we show in this paper that minmax tests have more appropriate properties and are better suited for the damage localization problem when the normalized parameter sensitivities are not orthogonal. In the context of vibration monitoring, minmax approaches have also been applied to damping monitoring (Zouari et al, 2009) or damage detection with temperature rejection (Balmès et al, 2009). While these studies have been focused on FDI, it has never been attempted to quantify the absolute change in the faulty parameter components.…”

Section: Introductionmentioning

confidence: 99%

Fault detection, isolation and quantification from Gaussian residuals with application to structural damage diagnosis

Döhler

Mevel

Zhang

2016

Annual Reviews in Control

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Despite the general acknowledgment in the Fault Detection and Isolation (FDI) literature that FDI are typically accomplished in two steps, namely residual generation and residual evaluation, the second step is by far less studied than the first one. This paper investigates the residual evaluation method based on the local approach to change detection and on statistical tests. The local approach has the remarkable ability of transforming quite general residuals with unknown or non Gaussian probability distributions into a standard Gaussian framework, thanks to a central limit theorem. In this paper, the ability of the local approach for fault quantification will be exhibited, whereas previously it was only presented for fault detection and isolation. The numerical computation of statistical tests in the Gaussian framework will also be revisited to improve numerical efficiency. An example of vibration-based structural damage diagnosis will be presented to motivate the study and to illustrate the performance of the proposed method.

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“…Based on a sensitivity model-based technique, Kess [21] used a variability test matrix to isolate operational and environmental variability for a woven composite plate and to minimize the variability of damage indicators. Based on a subspace residue and 2  -type global and sensitivity tests, Balmes [22] applied an extended detection algorithm to a simulated bridge deck. Xu and Wu [23] investigated the changes in frequencies and mode shape curvatures caused by average (seasonal) and asymmetric (sunshine) temperature variations in a cable-stayed bridge.…”

Section: Introductionmentioning

confidence: 99%

Data Fusion-Based Structural Damage Detection Under Varying Temperature Conditions

Bao

Xia

et al. 2012

Int. J. Str. Stab. Dyn.

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A huge number of data can be obtained continuously from a number of sensors in long-term structural health monitoring (SHM). Different sets of data measured at different times may lead to inconsistent monitoring results. In addition, structural responses vary with the changing environmental conditions, particularly temperature. The variation in structural responses caused by temperature changes may mask the variation caused by structural damages.Integration and interpretation of various types of data are critical to the effective use of SHM systems for structural condition assessment and damage detection. A data fusion-based damage detection approach under varying temperature conditions is presented. The Bayesian-based damage detection technique, in which both temperature and structural parameters are the variables of the modal properties (frequencies and mode shapes), is developed. Accordingly, the probability density functions of the modal data are derived for damage detection. The damage detection results from each set of modal data and temperature data may be inconsistent because of uncertainties. The Dempster-Shafer (D-S) evidence theory is then employed to integrate the individual damage detection results from the different data sets at different times to obtain a consistent decision. An experiment on a two-story portal frame is conducted to demonstrate the effectiveness of the proposed method, with consideration on model uncertainty, measurement noise, and temperature effect. The damage detection results obtained by combining the damage basic probability assignments from each set of test data are more accurate than those obtained from each test data separately. Eliminating the temperature effect on the vibration properties can improve the damage detection accuracy. In particular, the proposed technique can detect even the slightest damage that is not detected by common damage detection methods in which the temperature effect is not eliminated.

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Handling the temperature effect in vibration monitoring of civil structures: A combined subspace-based and nuisance rejection approach

Cited by 25 publications

References 24 publications

Vibration-Based Monitoring of Civil Structures with Subspace-Based Damage Detection

Vibration-Based Monitoring of Civil Structures with Subspace-Based Damage Detection

Fault detection, isolation and quantification from Gaussian residuals with application to structural damage diagnosis

Data Fusion-Based Structural Damage Detection Under Varying Temperature Conditions

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