Health indicators for diagnostics and prognostics of composite aerospace structures

Galanopoulos, Georgios; Milanoski, Dimitrios; Broer, Agnes; Zarouchas, Dimitrios; Λούτας, Θεόδωρος

doi:10.1109/metroaerospace51421.2021.9511759

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…46,52,[58][59][60] Also, model-independent methodologies are followed where solely experimental data are utilized. [61][62][63][64][65] Methods for damage identifications based on strains recorded from zero-strain trajectories are also employed. 35,66,67 Lately, strain-based SHM methodologies utilizing the inverse FE method are emerging.…”

Section: Strain-based Shm Methodologiesmentioning

confidence: 99%

Multi-level damage diagnosis on stiffened composite panels based on a damage-uninformative digital twin

Milanoski

Galanopoulos

Zarouchas

et al. 2022

Structural Health Monitoring

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In this study, a multi-level Structural Health Monitoring methodology for stiffened composite panels is introduced. A digital twin (DT), that is, a three-dimensional finite element (FE) model, representing the pristine state baseline of the test article, is developed and verified for compressive loading in the post-buckling regime. The detailed FE model is utilized to train a surrogate model with respect to exogenous input, that is, axial load magnitude. The surrogate assists the DT concept that would allow prediction of the load acting on the structure based on an influx of strain data, acquired from fiber Bragg grating sensors permanently attached along the stringer feet. For this purpose, we leverage on the observation that remote from the damage, the strain field remains virtually unaltered with regard to the pristine state. The load is estimated by a sensor placed far from the damage whilst the diagnostic actions are performed by exploiting measurements from the remaining sensing locations. A health indicator, which compares the experimentally received strains with those from the surrogate representing the pristine state, is utilized to (1) detect, (2) localize, and (3) characterize the damage. As damage, we consider either skin-to-stringer disbond or initial impact damage propagation as well as overall stiffness degradation during thousands or millions of fatigue cycles. The sensors that have detected a disbond are dedicated to evaluating the potential propagation of it, while the remaining sensors evaluate the overall stiffness degradation. The proposed methodology is tested for one artificially disbonded and two impacted single-stringer panels subjected to block loading compression-compression fatigue.

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Section: Strain-based Shm Methodologiesmentioning

confidence: 99%

Multi-level damage diagnosis on stiffened composite panels based on a damage-uninformative digital twin

Milanoski

Galanopoulos

Zarouchas

et al. 2022

Structural Health Monitoring

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“…The methodology presented above will be evaluated through a block loading compressioncompression fatigue test [19,25]. More specifically, one SSP containing an artificial skin/stringer disbond via a Teflon-insert is utilized.…”

Section: Test Campaignmentioning

confidence: 99%

Digital Twin-Based Damage Quantification on Composite Structures

Milanoski,

Galanopoulos,

Zarouchas

et al. 2023

10th ECCOMAS Thematic Conference on Smart Structures and Materials

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In the present study a digital twin-based methodology for Structural Health Monitoring (SHM) of composite stiffened panels is developed. More specifically, beyond damage detection and localization, the quantification of damage is determined based on relevant damaged training data provided by a numerical campaign. Parametric finite element models with distinct damage morphologies, i.e. skin-to-stringer delaminations, are generated based on a Latin hypercube sampling plan. The above sampling plan generates an adequate amount of simulated strain data capable of establishing a relation among the damage characteristics, i.e. damage size and location, and the longitudinal strain at specific sensing locations. Hence, Gaussian process (GP) surrogate models are trained with the numerically generated data and their hyperparameters are determined via Bayesian optimization. The damage quantification is treated as a minimization problem, the solution of which is obtained via a global optimization iterative procedure. The methodology is assessed utilizing a single-stringer composite panel with a rectangle skin/stringer artificial delamination. Compressive loads are applied on the panel and longitudinal static strains are received by permanently fiber Bragg grating sensors affixed onto the stringer feet. The pre-trained GP models are fed with experimental strains during testing and in turn yield the damage characteristics of the delamination. The methodology is also applicable to unknown load conditions as it predicts the load acting on the panel on the first SHM levels. Promising results are obtained, empowering the viewpoint of the proposed methodology which aims to harness the contemporary capabilities of numerical models toward real-time damage diagnosis on complicated structures.

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“…Note that the proposed framework is discussed on a high level in a conceptual manner, and merely serves to demonstrate the first indications of the enhanced capabilities of a multi-sensor-based SHM system. To fully unleash its potential, in-depth research studies are crucial that consist of experimental campaigns, detailed diagnostic and prognostic methodology development, and validation exercises [8,36,50,72,[89][90][91][92][93][94][95][96][97][98], which are beyond the scope of this work.…”

Section: Shm Framework Design For Composite Aircraft Structuresmentioning

confidence: 99%

The Need for Multi-Sensor Data Fusion in Structural Health Monitoring of Composite Aircraft Structures

2022

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With the increased use of composites in aircraft, many new successful contributions to the advancement of the structural health monitoring (SHM) field for composite aerospace structures have been achieved. Yet its application is still not often seen in operational conditions in the aircraft industry, mostly due to a gap between research focus and application, which constraints the shift towards improved aircraft maintenance strategies such as condition-based maintenance (CBM). In this work, we identify and highlight two key facets involved in the maturing of the SHM field for composite aircraft structures: (1) the aircraft maintenance engineer who requires a holistic damage assessment for the aircraft’s structural health management, and (2) the upscaling of the SHM application to realistic composite aircraft structures under in-service conditions. Multi-sensor data fusion concepts can aid in addressing these aspects and we formulate its benefits, opportunities, and challenges. Additionally, for demonstration purposes, we show a conceptual design study for a fusion-based SHM system for multi-level damage monitoring of a representative composite aircraft wing structure. In this manner, we present how multi-sensor data fusion concepts can be of benefit to the community in advancing the field of SHM for composite aircraft structures towards an operational CBM application in the aircraft industry.

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Health indicators for diagnostics and prognostics of composite aerospace structures

Cited by 7 publications

References 21 publications

Multi-level damage diagnosis on stiffened composite panels based on a damage-uninformative digital twin

Multi-level damage diagnosis on stiffened composite panels based on a damage-uninformative digital twin

Digital Twin-Based Damage Quantification on Composite Structures

The Need for Multi-Sensor Data Fusion in Structural Health Monitoring of Composite Aircraft Structures

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