Real time damage detection using recursive principal components and time varying auto-regressive modeling

Krishnan, Manu; Bhowmik, Basuraj; Hazra, Budhaditya; Pakrashi, Vikram

doi:10.1016/j.ymssp.2017.08.037

Cited by 57 publications

(63 citation statements)

References 58 publications

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“…e AR model is widely used in the eld of structural damage identication [18], and it is attempt to account for the correlations of the current time parameter with its predecessors in time series, in which the output variable depends linearly on its own previous values and on a stochastic term. It can be implemented to represent the dynamic response of structures [19]. e AR model does not need any speci c structural characteristics but the output response data; hence, it is widespread for complex structures [20,21].…”

Section: Ar Model and Parameter Identi Cationmentioning

confidence: 99%

Shaking Table Model Test and Seismic Performance Analysis of a High‐Rise RC Shear Wall Structure

Cai

Kong

2019

Shock and Vibration

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A building developed by Wuhan Shimao Group in Wuhan, China, is a high-rise residence with 56 stories near the Yangtze River. The building is a reinforced concrete structure, featuring with a nonregular T-type plane and a height 179.6 m, which is out of the restrictions specified by the China Technical Specification for Concrete Structures of Tall Building (JGJ3-2010). To investigate its seismic performance, a shaking table test with a 1/30 scale model is carried out in Structural Laboratory in Wuhan University of Technology. The dynamic characteristics and the responses of the model subject to different seismic intensities are investigated via the analyzing of shaking table test data and the observed cracking pattern of the scaled model. Finite element analysis of the shaking table model is also established, and the results are coincident well with the test. An autoregressive method is also presented to identify the damage of the structure after suffering from different waves, and the results coincide well with the test and numerical simulation. The shaking table model test, numerical analysis, and damage identification prove that this building is well designed and can be safely put into use. Suggestions and measures to improve the seismic performance of structures are also presented.

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Section: Ar Model and Parameter Identi Cationmentioning

confidence: 99%

Shaking Table Model Test and Seismic Performance Analysis of a High‐Rise RC Shear Wall Structure

Cai

Kong

2019

Shock and Vibration

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“…where x ij (t) is the normalized measurement signal, a k and b k are the k-th AR and MA coefficients, p and q are the model orders of the AR and MA processes, and ε ij (t) is the residual term. e algorithms of the group are discussed in detail [10,19,20]. In particular, the modified and implemented algorithm adapts to the structure in Figure 5.…”

Section: Structural Health Estimation and Damage Detectionmentioning

confidence: 99%

“…An interesting solution, presented by Krishnan et al [19], successfully eliminates the need for offline postprocessing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. is is a novel baseline-free approach for the continuous online damage detection of multidegree of freedom vibrating structures using recursive principal component analysis (RPCA) in conjunction with timevarying autoregressive (TVAR) modeling.…”

Section: Introductionmentioning

confidence: 99%

Effective Processing of Radar Data for Bridge Damage Detection

Owerko

Kuraś

2019

Shock and Vibration

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This paper presents the practical results of the evaluation of the data obtained by using ground-based radar interferometer during measurements carried out on bridge structures. Due to the nature of the objects studied, the authors proposed a comprehensive method of data analysis, which identifies whether the passage of the vehicle did not damage the bridge. The effective use of vehicles as a source of bridge excitation allowed us to first develop a method for determining the damping parameters resistant to potentially occurring beating frequencies. As a result, it is possible to determine these subsets of data registered with radar, for which it is possible to assume compliance with linear systems. This type of data, often omitted in other works, forms the basis for the second important element of the research—an algorithm based on the ARMA model supporting defect detection. The optimization of the performed calculations, in particular the proposed optimal ARMA model order, the method of fault identification based on the DSF parameter, or fault identification based on a nonmetrical Cook’s distance leads to a robust and scalable method. The method’s low computational complexity allows for implementation in real-time solutions. In addition, the distribution of errors and the sensitivity of classifiers based on the DSF parameter and Cook’s distances leaving them will enable the automation of the classification process using machine learning. The proposed method is universal; in particular, it can be used for radar interferometry methods because it is resistant to potentially variable environmental conditions.

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“…To obtain a systematic study on this new EC‐TMD, the force and energy‐based evaluation on the EC‐TMD controlled structures through shaking table test and field testing may be preferred for understanding the actuating and dissipation behavior in the time‐domain, which is challenging and may necessarily need the aid of SHM and SI. Currently, such energy‐based evaluation is widely used in theoretical and numerical studies on TMD (Lee et al., ; Lamarque et al., ; Reggio and Angelis, ), whereas the direct data interpretation and signal processing of measurements are mostly performed for the shaking table test (Rakicevic et al., ; Luo et al., ) and field test (Cho et al., ; Hazra et al., ; Shi et al., ; Yamamoto and Sone, ; Krishnan et al., ). The energy‐based effective damping and wavelet spectra (Luo et al., ) may be a very early effort to experimentally understand the energy dissipation behavior.…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Two‐Level Performance Evaluation of Eddy‐Current Tuned Mass Damper for Building Structures Using Shaking Table and Field Testing

Lü

Zhang

et al. 2018

Computer aided Civil Eng

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A computational framework with a two‐level performance evaluation is proposed for the tuned mass damper (TMD)‐structure system and is implemented for a new eddy‐current (EC) TMD in a data‐driven manner by incorporating the measurements from a shaking table test and real‐world field tests. The first level of evaluation corresponds to the data analysis with classical performance indices, whereas the second level focuses on the energy dissipation and exchange behavior in the time domain. Accordingly, two key steps of physical modeling and unmeasured response estimation are sequentially presented, and the steps adopt two types of energy balance formulations and state–space descriptions for the primary structure and the TMD, respectively. In the case of shaking table test, the momentary and cumulative behavior of energy dissipation and exchange at the second level is systematically revealed, and several key issues are identified for explaining the observed performance variation and excitation‐sensitivity. In the case of field test, the damping and control performance of the EC‐TMD at the real‐world scale is investigated with informative results using the first level evaluation.

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Real time damage detection using recursive principal components and time varying auto-regressive modeling

Cited by 57 publications

References 58 publications

Shaking Table Model Test and Seismic Performance Analysis of a High‐Rise RC Shear Wall Structure

Shaking Table Model Test and Seismic Performance Analysis of a High‐Rise RC Shear Wall Structure

Effective Processing of Radar Data for Bridge Damage Detection

Data‐Driven Two‐Level Performance Evaluation of Eddy‐Current Tuned Mass Damper for Building Structures Using Shaking Table and Field Testing

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