Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion

Ay, Ali Mete; Wang, Ying

doi:10.1177/1475921714542891

Cited by 36 publications

(20 citation statements)

References 25 publications

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“…The use of ARMA model in the process of feature extraction can be found in Carden and Brownjohn (2008), Chen and Yu (2013), and Zheng and Mita (2008). Finally, the ARMAX model has become more interesting among researchers to utilize in extracting DSFs (Ay and Wang, 2014; Sakellariou and Fassois, 2017; Wang et al, 2013).…”

Section: Time-series Analysismentioning

confidence: 99%

An iterative order determination method for time-series modeling in structural health monitoring

Rezaiee‐Pajand

Entezami

Shariatmadar

2017

Advances in Structural Engineering

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Statistical time-series modeling has recently emerged as a promising and applicable methodology to structural health monitoring. In this methodology, an important step is to choose robust and optimal orders of time-series models for extracting damage-sensitive features. In this study, an iterative order determination method is proposed to determine optimal orders based on residual analysis. The proposed technique consists of identifying the best time-series model, determining the maximum orders, and selecting the optimal orders that enable the model to extract uncorrelated residuals. The application of low-pass signal filters to the process of order determination is also evaluated. In a comparative study, the influence of optimal orders on damage is assessed to perceive whether features extracted from optimal models, with and without using low-pass filters, are sensitive to damage. Experimental data of a three-story laboratory frame and a large-scale bridge are applied to demonstrate the performance and capability of the proposed method. Results show that the proposed iterative method is an efficient tool for the determination of optimal orders along with the extraction of uncorrelated residuals.

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Section: Time-series Analysismentioning

confidence: 99%

An iterative order determination method for time-series modeling in structural health monitoring

Rezaiee‐Pajand

Entezami

Shariatmadar

2017

Advances in Structural Engineering

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“…Structural health monitoring [6,7] is one of the most effective technologies to sense the real response of the monitored objects. Many excellent monitoring technologies [8,9], systems [10], and advanced intelligent algorithms [11] have been developed and applied to solve engineering problems. A stress monitoring system [12,13], which plays an important role in structural health monitoring technologies, has been regarded as a mature way to obtain structural stress information from the macroscale stress distribution of a whole structure [14] to the microscale stress concentration of a local member [15].…”

Section: Introductionmentioning

confidence: 99%

Influence of Uniaxial Stress on the Shear-Wave Spectrum Propagating in Steel Members

Liu

et al. 2019

Sensors

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Structural health monitoring technologies have provided extensive methods to sense the stress of steel structures. However, monitored stress is a relative value rather than an absolute value in the structure’s current state. Among all the stress measurement methods, ultrasonic methods have shown great promise. The shear-wave amplitude spectrum and phase spectrum contain stress information along the propagation path. In this study, the influence of uniaxial stress on the amplitude and phase spectra of a shear wave propagating in steel members was investigated. Furthermore, the shear-wave amplitude spectrum and phase spectrum were compared in terms of characteristic frequency (CF) collection, parametric calibration, and absolute stress measurement principles. Specifically, the theoretical expressions of the shear-wave amplitude and phase spectra were derived. Three steel members were used to investigate the effect of the uniaxial stress on the shear-wave amplitude and phase spectra. CFs were extracted and used to calibrate the parameters in the stress measurement formula. A linear relationship was established between the inverse of the CF and its corresponding stress value. The test results show that both the shear-wave amplitude and phase spectra can be used to evaluate uniaxial stress in structural steel members.

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“…As another study, Sakaris et al 18 utilized Vector-dependent Functionally Pooled–Vector AutoRegressive with eXogenous input (VFP-VARX) model for damage precise localization in three-dimensional (3D) structural elements. Ay and Wang 19 proposed a damage identification methodology by employing AutoRegressive Moving Average with eXogenous (ARMAX) model to quantify the acquired set of vibration signals.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised learning approach by novel damage indices in structural health monitoring for damage localization and quantification

Entezami

Shariatmadar

2017

Structural Health Monitoring

124

101

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The aim of this article is to propose novel damage indices for damage localization and quantification based on time series modeling. In order to extract damage-sensitive features from time series models, it is essential to choose adequate and robust orders in such a way that the models are able to extract uncorrelated residuals. On this basis, a new iterative order determination method is proposed to select robust orders of time series models under residual analysis by Ljung–Box Q-test. The damage-sensitive features are the parameters and residuals of an AutoRegressive (AR) model obtained from current feature extraction approaches. In this study, the AR model is identified as the most compatible time series model with measured vibration time-domain responses using Box–Jenkins methodology and Leybourne–McCabe hypothesis test. The proposed damage indices are the parametric assurance criterion and the residual reliability criterion that exploit the parameters and residuals of AR models, respectively. The main idea behind locating a damage is to define threshold limits for both damage indices using the features of undamaged conditions based on an unsupervised learning way. The major contributions of this article are to propose an iterative order determination method for time series models and two novel damage indices for locating and quantifying damage. The accuracy and performance of the proposed methods are experimentally demonstrated on a three-story laboratory frame and a model-scale steel structure. Results show that the proposed iterative approach leads to uncorrelated residuals, and the proposed parametric assurance criterion and the residual reliability criterion methods are promising and efficient tools in damage detection problems under varying operational and environmental conditions.

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Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion

Cited by 36 publications

References 25 publications

An iterative order determination method for time-series modeling in structural health monitoring

An iterative order determination method for time-series modeling in structural health monitoring

Influence of Uniaxial Stress on the Shear-Wave Spectrum Propagating in Steel Members

An unsupervised learning approach by novel damage indices in structural health monitoring for damage localization and quantification

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