Damage classification and estimation in experimental structures using time series analysis and pattern recognition

Lautour, Oliver R. de; Omenzetter, Piotr

doi:10.1016/j.ymssp.2009.12.008

Cited by 112 publications

(57 citation statements)

References 49 publications

(43 reference statements)

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“…Among these, modal quantities (Alvandi & Crémona, 2006;Zhou, Ni, & Ko, 2011), wavelet analysis (Jung & Koh, 2009;Kim, Chong, Chon, & Kim, 2013) and autoregressive models (Figueiredo, 2010;Lautour & Omenzetter, 2010) have been reported as effective. When measured quantities comprise strain (Moyo & Bronjohn, 2002;Treacy & Brühwiler, 2012), displacements (Santos, Crémona, Orcesi, & Silveira, 2013) or forces (Calc ada, Cunha, & Delgado, 2005), time series contain a greater deal of structural-related information and feature extraction techniques usually aimed at characterising their statistical distributions.…”

Section: Introductionmentioning

confidence: 99%

Baseline-free real-time assessment of structural changes

Santos

Orcesi²,

Crémona³

et al. 2014

Structure and Infrastructure Engineering

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This article addresses the subject of data-driven structural health monitoring and proposes a real-time strategy to conduct structural assessment without the need to define a baseline period, in which the monitored structure is assumed healthy and unchanged. Independence from baseline references is achieved using unsupervised discrimination machine-learning methods, widely known as clustering algorithms, which are able to find groups in data relying only on their intrinsic features and without requiring prior knowledge as input. Real-time capability is based on the definition of symbolic data, which allows describing large amounts of information without loss of generality or structural-related information. The efficiency of the proposed methodology is illustrated using an experimental case study in which structural changes were imposed to a suspended bridge during an extensive rehabilitation programme. A single-value novelty index capable of describing multisensor data is proposed, and its effectiveness in identifying structural changes in real time, using outlier analysis, is discussed.

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Section: Introductionmentioning

confidence: 99%

Baseline-free real-time assessment of structural changes

Santos

Orcesi²,

Crémona³

et al. 2014

Structure and Infrastructure Engineering

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“…Among the forward approaches, the most prevalent techniques are those that combine data from different sensors without losing important information. Such approaches include principal component analysis (PCA) [33], time-frequency analysis [34], and autoregressive models [35]. PCA is a powerful tool for data reduction of highdimensional datasets without losing important information.…”

Section: Study Modelsmentioning

confidence: 99%

A Fiber Bragg Grating-Based Condition Monitoring and Early Damage Detection System for the Structural Safety of Underground Coal Mines Using the Internet of Things

Khan

Lee

et al. 2018

Journal of Sensors

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Accurate sensing is the key to structural health monitoring of underground coal mines while using fiber Bragg grating (FBG) sensors. However, the previously developed systems for structural monitoring of underground mines have been limited to monitoring without any capability of damage detection. Therefore, this study integrates a highly accurate FBG monitoring system and output-only data-driven approaches on an Internet of things (IoT)-based platform to develop a comprehensive mine structural safety system. This system relies on a Web 2.0 main server that runs data acquisition, data processing, and damage detection algorithms along with real-time information sharing at remote locations. This system was successfully implemented at the Hassan Kishore coal mine, situated in the Salt Range of Pakistan. Wavelength division multiplexing of the FBG strain sensors reliably captured the effects of dynamic and continuous coal excavation on the stability of mine roadway and access galleries. Principal component analysis, along with hierarchical clustering, was used to determine the damage indicator of the mine. The damage index was validated, showing the minimum value for 2% stiffness reduction. Thus, integration of FBG technology with the Internet can be effectively applied for early safety assessment of underground coal mines and information sharing in real time.

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“…It is necessary to use a technique that reduces the number of inputs that maintain the characteristic information of the signal. The feature coefficients of each ultrasonic signal are extracted using the AR model by the Yule-Walker method [48].…”

Section: Approach For Delamination Detection and Diagnosis In Wtbmentioning

confidence: 99%

Machine Learning for Wind Turbine Blades Maintenance Management

2017

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Delamination in Wind Turbine Blades (WTB) is a common structural problem that can generate large costs. Delamination is the separation of layers of a composite material, which produces points of stress concentration. These points suffer greater traction and compression forces in working conditions, and they can trigger cracks, and partial or total breakage of the blade. Early detection of delamination is crucial for the prevention of breakages and downtime. The main novelty presented in this paper has been to apply an approach for detecting and diagnosing the delamination WTB. The approach is based on signal processing of guided waves, and multiclass pattern recognition using machine learning. Delamination was induced in the WTB to check the accuracy of the approach. The signal is denoised by wavelet transform. The autoregressive Yule-Walker model is employed for feature extraction, and Akaike's information criterion method for feature selection. The classifiers are quadratic discriminant analysis, k-nearest neighbors, decision trees, and neural network multilayer perceptron. The confusion matrix is employed to evaluate the classification, especially the receiver operating characteristic analysis by: recall, specificity, precision, and F-score.

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Damage classification and estimation in experimental structures using time series analysis and pattern recognition

Cited by 112 publications

References 49 publications

Baseline-free real-time assessment of structural changes

Baseline-free real-time assessment of structural changes

A Fiber Bragg Grating-Based Condition Monitoring and Early Damage Detection System for the Structural Safety of Underground Coal Mines Using the Internet of Things

Machine Learning for Wind Turbine Blades Maintenance Management

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