Probabilistic SHM under varying loads via the integration of Gaussian Process Regression and physics-based guided-wave propagation models

Amer, Ahmad; Roy, Surajit; Kopsaftopoulos, Fotis

doi:10.2514/6.2021-0434

Cited by 7 publications

(7 citation statements)

References 18 publications

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“…In preliminary studies, the authors have also investigated GP Regression Models (GPRMs) 17,48 and GP Classification Models (GPCMs) 49 for the task of probabilistic damage quantification in active-sensing, guided-wave SHM. In addition, GPRMs were also used by the authors to predict damage size and load simultaneously 20,50 or with the assistance of physics-based models. 50 The aim of the present study is to expand on previous work using data-based GPRMs for damage quantification within active-sensing, guided-wave SHM in order to introduce and critically assess the performance and applicability of such models for different structures (metallic and composites), damage types and sizes, and static loading conditions.…”

Section: Introductionmentioning

confidence: 99%

Gaussian process regression for active sensing probabilistic structural health monitoring: experimental assessment across multiple damage and loading scenarios

Amer

Kopsaftopoulos

2022

Structural Health Monitoring

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In the near future, Structural Health Monitoring (SHM) technologies will be capable of overcoming the drawbacks in the current maintenance and life-cycle management paradigms, namely, cost, increased downtime, less-than-optimal safety management paradigm and the limited applicability of fully-autonomous operations. One of the most challenging tasks is structural damage quantification. Existing quantification techniques face accuracy and/or robustness issues when it comes to varying operating and environmental conditions. In addition, the damage/no-damage paradigm of current industrial frameworks does not offer much information to maintainers on the ground for proper decision-making. In this study, a novel structural damage quantification framework is proposed based on the widely-used Damage Indices (DIs) and Gaussian Process Regression Models (GPRMs) in order to overcome the aforementioned shortcomings. The proposed framework takes a simple approach to the damage quantification problem by using DI values for training, and provides confidence bounds on the estimated states. In addition, the proposed method is shown to quantify multiple structural states simultaneously from incoming DI test points. This framework is applied to three test cases: a Carbon Fibre-Reinforced Plastic (CFRP) coupon with attached weights as simulated damage, an aluminum coupon with a notch and an aluminum coupon with attached weights as simulated damage under varying loading states. The novel state prediction method presented herein is applied to single-state quantification in the first two test cases, as well as the third one assuming the loading state is known. Finally, the proposed method is applied to the third test case assuming neither the damage size nor the load is known in order to predict both simultaneously from incoming DI test points. In applying this framework, two forms of GPRMs (standard and variational heteroscedastic) are used in order to critically assess their performance with respect to the three test cases.

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Section: Introductionmentioning

confidence: 99%

Gaussian process regression for active sensing probabilistic structural health monitoring: experimental assessment across multiple damage and loading scenarios

Amer

Kopsaftopoulos

2022

Structural Health Monitoring

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“…It has been proved that the minimization of the tilted/ pinball loss function given by equation ( 11) is exactly equivalent to the maximization of a likelihood function as equation (15). Detailed derivation can be found in reference 30 .…”

Section: Guided Wave-based Heteroscedastic Gaussian Process Methods A...mentioning

confidence: 99%

“…A more accurate crack evaluation of individual aircraft structures in service was realized. Amer et al 15 proposed a probabilistic SHM framework by integrating physics-based models with the GP model. The physics-based model was utilized to compensate the load effects under different load situations.…”

Section: Introductionmentioning

confidence: 99%

A new GW-based heteroscedastic Gaussian process method for online crack evaluation

Wang

Yuan

et al. 2022

Structural Health Monitoring

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Accurate evaluation of fatigue crack by using structural health monitoring (SHM) methods is very important to the life management of aircraft structures. However, fatigue crack propagation is always a complicated process for individual aircraft structures during their long-term service. And the monitoring has to be performed under different environmental and operational conditions. These factors result in the uncertain distribution of the damage index obtained by SHM. The distribution usually changes along with the service time, which can be defined as heteroscedasticity. The heteroscedasticity characteristic of the uncertain distribution of damage index has an important negative impact on the SHM based diagnostics algorithms if not considered during the evaluation. However, till now, few researchers have considered this important aspect. To improve the evaluation accuracy under different environmental and operational conditions for individual aircraft, this paper proposes a new guided wave-based heteroscedastic Gaussian process method. Gaussian process quantile regression is adopted to estimate the conditional quantiles of the damage index distribution during the service to deal with the heteroscedasticity. The method is validated on an attachment lug fatigue test, an important aircraft structural component. The experimental results demonstrate that the proposed method can quantify the heteroscedastic uncertainty associated and obviously improve the quality of crack evaluation. For the serious heteroscedastic specimen, the maximum evaluation is only 0.7 mm reduced from the original 7.4 mm, which is an order of magnitude.

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“…The most widely used method for damage detection using guided waves is the concept of damage/health indices/indicators(D/HI). The idea is that the features of the signal from an unknown structural state are compared to that coming from the healthy structure [15][16][17][18][19][20][21]. These features may be based on the specific mode wave packets of the guided wave signal, the amplitude/magnitude, or the energy content of the signal.…”

Section: Acronymsmentioning

confidence: 99%

Stochastic Identification-based Active Sensing Acousto-Ultrasound SHM Using Stationary Time Series Models

Ahmed¹,

Kopsaftopoulos²

2022

Preprint

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In this work, a probabilistic damage detection and identification scheme using stochastic time series models in the context of acousto-ultrasound guided wave-based SHM is proposed, and its performance is assessed experimentally. In order to simplify the damage detection and identification process, model parameters are modified based on the singular value decomposition (SVD) as well as the principal component analysis (PCA)-based truncation approach. The modified model parameters are then used to estimate a statistical characteristic quantity that follows a chi-squared distribution. A probabilistic threshold is used instead of a user-defined margin to facilitate automatic damage detection. The method's effectiveness is assessed via multiple experiments using both metallic and composite coupons and under various damage scenarios using damage intersecting and damage non-intersecting paths. The results of the study confirm the high potential and effectiveness of the stochastic time series methods for guided wave-based damage detection and identification in a potentially automated way.

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Probabilistic SHM under varying loads via the integration of Gaussian Process Regression and physics-based guided-wave propagation models

Cited by 7 publications

References 18 publications

Gaussian process regression for active sensing probabilistic structural health monitoring: experimental assessment across multiple damage and loading scenarios

Gaussian process regression for active sensing probabilistic structural health monitoring: experimental assessment across multiple damage and loading scenarios

A new GW-based heteroscedastic Gaussian process method for online crack evaluation

Stochastic Identification-based Active Sensing Acousto-Ultrasound SHM Using Stationary Time Series Models

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