Damage classification in structural health monitoring using principal component analysis and self-organizing maps

Burgos, Diego Alexander Tibaduiza; Mujica, Luis Eduardo; Rodellar, José

doi:10.1002/stc.1540

Cited by 108 publications

(77 citation statements)

References 31 publications

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“…This feature vector is then introduced as the input to a classifier. A SOM has been chosen as classifier because its characteristics can provide a good support for the classification and graphical representation and grouping input data with similar features in clusters [5]. One important characteristic of this kind of ANN is that it does not need previous knowledge about the state of the structure (healthy or with some damage) to obtain the final clustering.…”

Section: Damage Detection Methodologymentioning

confidence: 99%

Principal Component Analysis and Self-organizing Maps for Damage Detection and Classification under Temperature Variations

Vejar¹,

Burgos²,

Torres-Arredondo³

et al. 2015

Structural Health Monitoring 2015

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Section: Damage Detection Methodologymentioning

confidence: 99%

Principal Component Analysis and Self-organizing Maps for Damage Detection and Classification under Temperature Variations

Vejar¹,

Burgos²,

Torres-Arredondo³

et al. 2015

Structural Health Monitoring 2015

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“…The total weight of the entries in various units come out of an output [36,[38][39][40]. Recently, the SOFM has become more frequently used when solving civil engineering problems [41][42][43][44][45]. In this research, better prediction results are more probable with the use of the SOFM than with the use of previous models.…”

Section: Introductionmentioning

confidence: 98%

Prediction of the Corrosion Current Density in Reinforced Concrete Using a Self-Organizing Feature Map

2017

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A disadvantage of using linear polarization resistance (LPR) in the measurement of corrosion current density is the need to partially destroy a concrete cover. In this article, a new technique of predicting the corrosion current density in reinforced concrete using a self-organizing feature map (SOFM) is presented. For this purpose, air temperature, and also the parameters determined by the resistivity four-probe method and galvanostatic resistivity measurements, were employed as input variables. The corrosion current density, predicted by the destructive LPR method, was employed as the output variable. The weights of the SOFM were optimized using the genetic algorithm (GA). To evaluate the accuracy of the SOFM, a comparison with the radial basis function (RBF) and linear regression (LR) was performed. The results indicate that the SOFM-GA model has a higher ability, flexibility, and accuracy than the RBF and LR.

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“…The authors have been recently using PCA in SHM applications, specifically for damage classification using self-organizing maps (Tibaduiza et al, 2012 and damage localization tasks (Tibaduiza et al, 2011). In these works, data from the structure are collected by a piezoelectric system in several actuation phases.…”

Section: Introductionmentioning

confidence: 99%

Structural damage detection using principal component analysis and damage indices

Burgos

Mujica

Rodellar

et al. 2015

Journal of Intelligent Material Systems and Structures

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One of the most important tasks in structural health monitoring corresponds to damage detection. In this task, the existence of damage should be determined. In the literature, several potentially useful techniques for damage detection can be found, and their applicability to a particular situation depends on the size of the critical damages that are admissible in the structure. Almost all of these techniques follow the same general procedure: the structure is excited using actuators, and the dynamical response is sensed at different locations throughout the structure. Any damage will change this vibrational response. The state of the structure is diagnosed by means of the processing of these data. Several studies have shown that the detection of changes in a structure depends on the distance from the damage to the actuator as well as the configuration of the sensor network. In this article, the authors considered the advantage of using an active piezoelectric system, where the lead zirconate titanate transducers are used as actuator and sensors in different actuation phases. In each actuation phase of the diagnosis procedure, one lead zirconate titanate transducer is used as actuator (a known electrical signal is applied), and the others are used as sensors (collecting the wave propagated through the structure at different points). An initial baseline model for undamaged structure is built applying principal component analysis to the data collected by several experiments and after the current structure (damaged or not) is subjected to the same experiments, and the collected data are projected into the principal component analysis models. Two of these projections and four damage indices (T 2 -statistic, Q-statistic, combined index, and I 2 index) by each actuation phase are used to determine the presence of damages and to distinguish between them. These indices are calculated based on the analysis of the residual data matrix to represent the variability of the data projected within the residual subspace and the new space of the principal components. To validate the approach, data from two aeronautical structures-an aircraft skin panel and an aircraft turbine blade-are used.

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Damage classification in structural health monitoring using principal component analysis and self-organizing maps

Cited by 108 publications

References 31 publications

Principal Component Analysis and Self-organizing Maps for Damage Detection and Classification under Temperature Variations

Principal Component Analysis and Self-organizing Maps for Damage Detection and Classification under Temperature Variations

Prediction of the Corrosion Current Density in Reinforced Concrete Using a Self-Organizing Feature Map

Structural damage detection using principal component analysis and damage indices

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