Application of structural health monitoring techniques to composite wing panels

Romano, Fulvio; Ciminello, Monica; Sorrentino, Assunta; Mercurio, Umberto

doi:10.1177/0021998319843333

Cited by 18 publications

(13 citation statements)

References 36 publications

(73 reference statements)

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“…The optical fiber does not need any engraved sensors and they provide strain readings with a much denser spatial resolution than FBGs. Researchers from several fields showed an intense interest on SHM applications utilizing distributing sensing technique over the last years (Glisic and Inaudi, 2012; Romano et al, 2019). A non-model-based SHM system was proposed by Ciminello et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Strain-based health indicators for the structural health monitoring of stiffened composite panels

Milanoski

Λούτας

2020

Journal of Intelligent Material Systems and Structures

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A common defect of composite-stiffened structures is the disbond at the interface between the two constituents (skin/stringer), as a result of inefficient manufacturing process or foreign object impacts in service. Generally, discontinuities within the volume of an elastic solid medium, subjected to mechanical load, cause anomalies on the strain field in the near vicinity of the discontinuity. Utilizing this observation, this work investigates the effect of artificially induced disbonds in the skin/stiffener interface of an aeronautical-grade generic element. A structural health monitoring methodology is developed, leveraging on numerically simulated strains along the stringer foot which aims to assess the health state of the panel as the size of the disbonds increases. The study is implemented using a parametric finite element model generating various disbond scenarios. Longitudinal strain values are acquired at the exact points where in reality actual fiber Bragg grating sensors will be located. Two types of commonly utilized strain-based health indicators are evaluated, and their drawbacks are revealed and discussed. A new health indicator is proposed that proves its capability to monitor growing disbonds while being both load- and baseline-independent.

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Section: Introductionmentioning

confidence: 99%

Strain-based health indicators for the structural health monitoring of stiffened composite panels

Milanoski

Λούτας

2020

Journal of Intelligent Material Systems and Structures

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“…In the near future, structural health monitoring (SHM) systems will be capable of implementing all four levels of SHM, namely: damage detection, localization, quantification, and remaining useful life estimation (prognosis), [1][2][3][4][5] with sustainable levels of performance in complex components under varying operational and environmental conditions. In order to reach this milestone, a number of common challenges facing current SHM techniques in different fields, that is, aerospace, mechanical, and civil structural systems, needs to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Statistical guided-waves-based structural health monitoring via stochastic non-parametric time series models

Amer

Kopsaftopoulos

2021

Structural Health Monitoring

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Damage detection in active-sensing, guided-waves-based structural health monitoring (SHM) has evolved through multiple eras of development during the past decades. Nevertheless, there still exist a number of challenges facing the current state-of-the-art approaches, both in the industry as well as in research and development, including low damage sensitivity, lack of robustness to uncertainties, need for user-defined thresholds, and non-uniform response across a sensor network. In this work, a novel statistical framework is proposed for active-sensing SHM based on the use of ultrasonic guided waves. This framework is based on stochastic non-parametric time series models and their corresponding statistical properties in order to readily provide healthy confidence bounds and enable accurate and robust damage detection via the use of appropriate statistical decision-making tests. Three such methods and corresponding statistical quantities (test statistics) along with decision-making schemes are formulated and experimentally assessed via the use of three coupons with different levels of complexity: an Al plate with a growing notch, a carbon fiber-reinforced plastic (CFRP) plate with added weights to simulate local damage, and the CFRP panel used in the Open Guided Waves project, all fitted with piezoelectric transducers under a pitch-catch configuration. The performance of the proposed methods is compared to that of state-of-the-art time-domain damage indices (DIs). The results demonstrate the increased detection sensitivity and robustness of the proposed methods, with better tracking capability of damage evolution compared to conventional approaches, even for damage-non-intersecting actuator–sensor paths. In particular, the Z statistic emerges as the best damage detection metric compared to conventional DIs, as well as the other proposed statistics. Overall, the proposed statistics in this study promise greater damage sensitivity across different components, with enhanced robustness to uncertainties, as well as user-friendly application.

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“…Because of the complexity of aircraft operations, manifested in multiple operational cycles, and, within each cycle, the varying operational and environmental conditions, the aerospace industry poses as a very rich arena for development of SHM techniques [6]. By convention, a complete SHM system is one that can implement all 4 levels of SHM with high accuracy and robustness, namely: damage detection, localization, quantification and remaininguseful-life estimation [9][10][11][12]. When it comes to SHM in aerospace structures today, the most common metric used for interrogating structural health is composed of one or more Damage Indices (DIs).…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Regression for Active Sensing Probabilistic Structural Health Monitoring: Experimental Assessment Across Multiple Damage and Loading Scenarios

Amer¹,

Kopsaftopoulos²

2021

Preprint

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In the near future, Structural Health Monitoring (SHM) technologies for aircraft 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. In the context of SHM for aircraft structures, one of the most challenging tasks is structural damage quantification. Currentlyutilized quantification techniques face accuracy and/or robustness issues when it comes to the varying operating and environmental conditions involved in day-to-day operations. 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 quantified states using a novel state prediction technique. The novelty in this approach to state quantification lies in calculating the probability of an incoming test DI point originating from a specific state, which allows for probability-educated decision-making. In addition, the proposed methods are shown to quantify multiple structural states simultaneously from incoming DI test points. This framework is applied to three test cases: a Carbon Fiber-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 performances with respect to the three test cases.

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Application of structural health monitoring techniques to composite wing panels

Cited by 18 publications

References 36 publications

Strain-based health indicators for the structural health monitoring of stiffened composite panels

Strain-based health indicators for the structural health monitoring of stiffened composite panels

Statistical guided-waves-based structural health monitoring via stochastic non-parametric time series models

Gaussian Process Regression for Active Sensing Probabilistic Structural Health Monitoring: Experimental Assessment Across Multiple Damage and Loading Scenarios

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