Novelty detection and dimension reduction via guided ultrasonic waves: Damage monitoring of scarf repairs in composite laminates

Pavlopoulou, S.; Worden, Keith; Soutis, C.

doi:10.1177/1045389x15574937

Cited by 32 publications

(27 citation statements)

References 30 publications

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“…Thus a statistical analysis is needed to consider these various uncertainties. Since outlier analysis have been demonstrated as a robust unsupervised learning pattern recognition tool for damage detection [40][41][42][43][44], it is employed in this study to analyze the WPT features obtained from the response guided wave signals under different fatigue states.…”

Section: Statistical Multivariate Outlier Analysismentioning

confidence: 99%

Guided Wave-Based Monitoring of Evolution of Fatigue Damage in Glass Fiber/Epoxy Composites

Yan

Tang

2019

Applied Sciences

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This paper presents an experimental study on detecting and monitoring of evolution of fatigue damage in composites under cyclic loads by using guided waves. Composite specimens fabricated by glass fiber/epoxy laminates and surface mounted with piezoelectric wafers are fatigued under tension–tension loads. A laser extensometer is used to obtain the degradation of longitudinal stiffness of the specimens under fatigue states to reflect the accumulation of internal fatigue damage. Meanwhile, at different fatigue cycles, one wafer acts as actuator to excite diagnostic guided waves, and the other acts as sensor to receive corresponding response waves. These guided wave signals are then processed by wavelet packet transform to extract characteristic features of energies in multiple frequency bands. A statistical multivariate outlier analysis is then performed to determine the existence of fatigue damage and to characterize their evolution using Mahalanobis squared distance. Experimental results have demonstrated the potential applicability and effectiveness of guided waves for continuous monitoring of fatigue damage in composite structures.

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Section: Statistical Multivariate Outlier Analysismentioning

confidence: 99%

Guided Wave-Based Monitoring of Evolution of Fatigue Damage in Glass Fiber/Epoxy Composites

Yan

Tang

2019

Applied Sciences

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“…The choice of the more interesting approach to be used it depends on the specific problem and on the level of knowledge about the damage (detection, location, assessment, prediction) desired [8]. In this sense, a lot of methods can be used, such as linear and nonlinear Principal Component Analysis (PCA -NPCA) [9], Extreme Value Statistics (EVS) [10], Peaks Over Threshold (POT) [11], machine learning algorithms [12], neural network [13], Bayesian approaches [14], Mahalanobis distance [15,16], and others. In all these applications, the final objective of the methods used are the same, differentiate the uncertainties and variabilities of the damages.…”

Section: Introductionmentioning

confidence: 99%

Damage detection in uncertain nonlinear systems based on stochastic Volterra series

Villani

Silva

Cunha

2019

Mechanical Systems and Signal Processing

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The damage detection problem in mechanical systems, using vibration measurements, is commonly called Structural Health Monitoring (SHM). Many tools are able to detect damages by changes in the vibration pattern, mainly, when damages induce nonlinear behavior. However, a more difficult problem is to detect structural variation associated with damage, when the mechanical system has nonlinear behavior even in the reference condition. In these cases, more sophisticated methods are required to detect if the changes in the response are based on some structural variation or changes in the vibration regime, because both can generate nonlinearities. Among the many ways to solve this problem, the use of the Volterra series has several favorable points, because they are a generalization of the linear convolution, allowing the separation of linear and nonlinear contributions by input filtering through the Volterra kernels. On the other hand, the presence of uncertainties in mechanical systems, due to noise, geometric imperfections, manufacturing irregularities, environmental conditions, and others, can also change the responses, becoming more difficult the damage detection procedure. An approach based on a stochastic version of Volterra series is proposed to be used in the detection of a breathing crack in a beam vibrating in a nonlinear regime of motion, even in reference condition (without crack). The system uncertainties are simulated by the variation imposed in the linear stiffness and damping coefficient. The results show, that the nonlinear analysis done, considering the high order Volterra kernels, allows the approach to detect the crack with a small propagation and probability confidence, even in the presence of uncertainties.

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“…Additionally, systems' measured output can exhibit data variability from sources such as environmental or input load variation, aleatoric noise, changes in boundary conditions, variations in the fabrication processes (i.e., unit-to-unit variability), and others [21][22][23] . These variations all confound the damage detection process, suggesting the use of probabilistic tools [24][25][26] , regression models 5,27 , machine learning algorithms 28 , probabilistic model selection approaches [29][30][31][32] , outlier analysis 33,34 and novelty detection methods 5 . Considering the data variation problem is important in reducing the number of false alarms 10,35 , although there are situations where variability can mask positive detections as well.…”

Section: Introductionmentioning

confidence: 99%

On the detection of a nonlinear damage in an uncertain nonlinear beam using stochastic Volterra series

Villani

Silva

Cunha

et al. 2019

Structural Health Monitoring

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In the present work, two issues that can complicate a damage detection process are considered: the uncertainties and the intrinsically nonlinear behavior. To deal with these issues, a stochastic version of the Volterra series is proposed as a baseline model, and novelty detection is applied to distinguish the condition of the structure between a reference baseline state (presumed “healthy”) and damaged. The studied system exhibits nonlinear behavior even in the reference condition, and it is exposed to a type of damage that causes the structure to display a nonlinear behavior with a different nature than the initial one. In addition, the uncertainties associated with data variation are taken into account in the application of the methodology. The results confirm that the monitoring of nonlinear coefficients and nonlinear components of the system response enables the method to detect the presence of the damage earlier than the application of some linear-based metrics. Besides that, the stochastic treatment enables the specification of a probabilistic interval of confidence for the system response in an uncertain ambient, thus providing more robust and reliable forecasts.

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Novelty detection and dimension reduction via guided ultrasonic waves: Damage monitoring of scarf repairs in composite laminates

Cited by 32 publications

References 30 publications

Guided Wave-Based Monitoring of Evolution of Fatigue Damage in Glass Fiber/Epoxy Composites

Guided Wave-Based Monitoring of Evolution of Fatigue Damage in Glass Fiber/Epoxy Composites

Damage detection in uncertain nonlinear systems based on stochastic Volterra series

On the detection of a nonlinear damage in an uncertain nonlinear beam using stochastic Volterra series

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