A Fuzzy-set-based Joint Distribution Adaptation Method for Regression and its Application to Online Damage Quantification for Structural Digital Twin

Online damage quantification suffers from insufficient labeled data. In this context, adopting the domain adaptation on historical labeled data from similar structures/damages to assist the current diagnosis task would be beneficial. However, most domain adaptation methods are designed for classification and cannot efficiently address damage quantification, a regression problem with continuous real-valued labels. This study first proposes a novel domain adaptation method, the Online Fuzzy-set-based Joint Distribution Adaptation for Regression, to address this challenge. By converting the continuous real-valued labels to fuzzy class labels via fuzzy sets, the conditional distribution discrepancy is measured, and domain adaptation can simultaneously consider the marginal and conditional distribution for the regression task. Furthermore, a framework of online damage quantification integrated with the proposed domain adaptation method is presented. The method has been verified with an example of a damaged helicopter panel, in which domain adaptations are conducted across different damage locations and from simulation to experiment, proving the accuracy of damage quantification can be improved significantly even in a noisy environment. It is expected that the proposed approach to be applied to the fleet-level digital twin considering the individual differences.Comment: 29 pages, 10 figure

Experimental and numerical study of the influence of pre-existing impact damage on the low-velocity impact response of CFRP panels

This paper presents an experimental and numerical investigation on the influence of preexisting impact damage on the low-velocity impact response of Carbon Fiber Reinforced Polymer (CFRP). A continuum damage mechanics-based material model was developed by defining a userdefined material model in Abaqus/Explicit. The model employed the action plane strength of Puck for the damage initiation criterion together with a strain-based progressive damage model. Initial finite element simulations at the single-element level demonstrated the validity and capability of the damage model. More complex models were used to simulate tensile specimens, coupon specimens, and skin panels subjected to low-velocity impacts, being validated against experimental data at each stage. The effect of non-central impact location showed higher impact peak forces and bigger damage areas for impacts closer to panel boundaries. The presence of pre-existing damage close to the impact region leading to interfering delamination areas produced severe changes in the mechanical response, lowering the impact resistance on the panel for the second impact, while for noninterfering impacts, the results of the second impact were similar to the impact of a pristine specimen

modelling and experimental testing of thick cfrp composites subjected to low velocity impacts

Abstract The present paper investigates a modelling approach of experimentally tested thick panels made of Carbon Fibre Reinforced Polymers (CFRP). The coupons were made of 24 unidirectional (UD) laminae with a layup [45/0/-45/90]3s. The specimens were subjected to low velocity impact using a drop tower system. Several sensors, including a load cell and strain gauge, were utilized both for analysing the behaviour of the material against the impact and for performing a validation of the numerical models. Three energy levels were adopted: 8J, 10J and 12J. Numerical models were implemented into the finite element (FE) software LS-DYNA. A linear - elastic constitutive law with an instantaneous failure material was selected for mimicking the intralaminar behaviour of the carbon fibre composite. Enhanced Chang – Chang was adopted as the onset-of-failure criterion. This criterion is able to capture damage in different directions and permits the consideration (or not) of the shear behaviour in the failure equations. The capability of the model to capture the correct interface failure process was particularly emphasized and therefore cohesive elements with a bilinear traction – separation law were chosen for the reproduction of delamination. Finally, the experimental – numerical results were compared using first and foremost the overall delamination area and the curves force – time, force – displacement and absorbed energy – time as well as the strain measures obtained by the sensors

Strain wave acquisition by a fiber optic coherent sensor for impact monitoring

A novel fiber optic sensing technology for high frequency dynamics detection is proposed in this paper, specifically tailored for structural health monitoring applications based on strain wave analysis, for both passive impact identification and active Lamb wave monitoring. The sensing solution relies on a fiber optic-based interferometric architecture associated to an innovative coherent detection scheme, which retrieves in a completely passive way the high-frequency phase information of the received optical signal. The sensing fiber can be arranged into different layouts, depending on the requirement of the specific application, in order to enhance the sensor sensitivity while still ensuring a limited gauge length if punctual measures are required. For active Lamb wave monitoring, this results in a sensing fiber arranged in multiple loops glued on an aluminum thin panel in order to increase the phase signal only in correspondence to the sensing points of interest. Instead, for passive impact identification, the required sensitivity is guaranteed by simply exploiting a longer gauge length glued to the structure. The fiber optic coherent (FOC) sensor is exploited to detect the strain waves emitted by a piezoelectric transducer placed on the aluminum panel or generated by an impulse hammer, respectively. The FOC sensor measurements have been compared with both a numerical model based on Finite Elements and traditional piezoelectric sensors, confirming a good agreement between experimental and simulated results for both active and passive impact monitoring scenarios

Numerical and experimental verification of an inverse-direct approach for load and strain monitoring in aeronautical structures

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Analytical Model to Describe Damage in CFRP Specimen When Subjected to Low Velocity Impacts

Abstract In the light of an increment of the safety of CFRP components subjected to low-velocity impacts, the identification of a damage onset threshold is desired. Hence the suitability of an analytical model for the estimation of the critical load of delamination onset and the resulting delaminated area, as well as for the approximation of the load-displacement curve, has been investigated. The Olsson's analytical model, available in the literature, is considered and applied in this study for the prediction of the mechanical behaviour of composite specimens subject to low-velocity impacts. Comparisons with experimental results have been carried out to demonstrate the accuracy of the presented model. Impact tests were performed in accordance with ASTM D7136 standard and damage was assessed by means of ultrasonic testing and computed tomography

Optimization of an artificial neural network for fatigue damage identification using analysis of variance

Artificial neural networks (ANN) are extensively utilized in structural health monitoring. Nevertheless, the definition of a rigorous method for the optimization of their structure is still an unresolved issue, especially when applied to safety critical systems. In this paper, an approach typically adopted in the design of experiments and based on the analysis of variance (ANOVA) is used to statistically determine the number of hidden neurons in a three-layer ANN structure. Repeated trainings of the same network structure provide multiple observations of the performance index here, based on the root mean square error. Different levels of network structure complexity are statistically compared, based on the number of hidden nodes. ANOVA is used to determine whether there is statistical evidence that the network performance is influenced by the number of hidden nodes. This analysis allows defining the threshold number of hidden nodes above which there is no statistical evidence of a performance benefit by the increase of the ANN structure complexity. The method is applied to the optimization of a set of algorithms for the diagnosis of fatigue damage on a typical aeronautical structure, consisting of a metallic panel with a riveted skin-stringer construction. The ANNs for damage detection, localization, and quantification are trained and validated with finite element simulated strain data and are finally tested on experimental strain signals, acquired in real-time in a fatigue crack growth laboratory test program including a skin crack artificially initiated in a panel bay and two stringers that had failed naturally under fatigue load

Sequential importance sampling based on a committee of Artificial Neural Networks for posterior health condition estimation

International audienceThe output of real-time diagnostic systems based on the interpretation of signals from a sensor network is often affected by very large uncertainties if compared with local non-destructive testing methods. Sequential Importance Resampling (SIR) is used in this study to filter the output distribution from a committee of Artificial Neural Networks. The methodology is applied to a helicopter panel subject to fatigue crack propagation. Strain signals are acquired during crack evolution and a diagnostic unit trained on simulated experience provides damage assessment in real-time. This information is filtered through a SIR routine, providing model identification, model parameter estimation and crack length probability density function updating, conditioned on the observations at discrete time steps

Performance qualification of an on-board model-based diagnostic system for fatigue crack monitoring

Very little success has been reported in the literature in developing diagnostic systems trained on simulated data that can accurately describe real situations. Furthermore, very few studies attempting distributed structural health monitoring (SHM) system performance qualification are available. A diagnostic algorithm based on an artificial neural network and trained with finite element simulated strains has been verified during repeated fatigue crack growth tests on metallic helicopter fuselage panels. Strain measures from a network of fiber Bragg gratings are provided as input to the diagnostic system, allowing fatigue crack damage identification. Anomaly detection performances have been evaluated with reference to the recent Aerospace Recommended Practice (ARP-6461) and the Recommended Practice for a Demonstration of Non Destructive Evaluation Reliability on Aircraft Production Parts, providing a SHM system qualification in terms of minimum detectable crack length, based on reliability-confidence curves. Furthermore, the numericalmodel of themonitored structure has been used for the generation of virtual specimens, thus predicting system performances in a model-assisted framework

Performance qualification of an on-board model-based diagnostic system for fatigue crack monitoring

Very little success has been reported in the literature in developing diagnostic systems trained on simulated data that can accurately describe the real situation. Furthermore, no studies attempting automated structural health monitoring (SHM) system performance qualification are available. A diagnostic algorithm based on an artificial neural network and trained with finite element simulated strains has been verified during repeated fatigue crack growth tests on metallic helicopter fuselage panels. Strain measures from a network of fiber Bragg gratings are provided as input to the diagnostic system, allowing fatigue crack damage identification. Anomaly detection performances have been evaluated with reference to the recent Aerospace Recommended Practice (ARP-6461) and the Recommended Practice for a Demonstration of Nondestructive Evaluation Reliability on Aircraft Production Parts, providing a SHM system qualification in terms of minimum detectable crack length, based on reliability-confidence curves. In particular, repeated fatigue crack growth tests on metallic aerospace panels allowed the estimation of the probability of detection as a function of crack length. Furthermore, a numerical model of the monitored structure has been used for the generation of virtual specimens, thus enabling the model-assisted probability of detection assessment