Sensor network optimization for damage detection on aluminium stiffened helicopter panels

Health and Usage Monitoring Systems (HUMS) has received considerable attention from the helicopter community in recent years with the declared aim to increase flight safety, increase mission reliability, extend duration of life limited components and of course reduce the maintenance costs. The latter is about 25 per cent of the direct operating cost of the helicopter, thus playing an important role especially in the case of the ageing aircrafts. In particular, with respect to helicopter fuselages, only some attempts were carried out to monitor directly on-line the damage accumulation and propagation during life. In this field, and in particular in the military applications, an integrated and reliable system for monitoring the damage in the fuselage and for evaluating the time inspections and remaining life (prognosis) is missing. However, because of the presence of many vibratory loads, the diagnosis of helicopter structures is very critical. From one hand, a very large number of sensors would be needed for a robust appreciation of the structural health, from the other hand the industrialization of the product brings the need for a low impact over the existing structures, or toward a reduction in the allowed amount of sensors. As a result, comes the importance for an optimization of the sensor network, with the aim to find out the regions inside the structure which are the most sensible to a damage and at the same time robust to noise. The aim of the present work is to define a methodology for optimising the sensors position inside an helicopter fuselage panel in order to obtain the best compromise between the simplicity and the robustness of a sensor network. In particular, a Finite Element (FE) model will be used to create a database of various damages inside the structure, thus consequently optimising the network sensitivity to any damage. The evaluation of the network performances is provided when some realistic noise [1,2] is added to the FE calculation

Towards Automatic Crack Size Estimation with iFEM for Structural Health Monitoring

The inverse finite element method (iFEM) is a model-based technique to compute the displacement (and then the strain) field of a structure from strain measurements and a geometrical discretization of the same. Different literature works exploit the error between the numerically reconstructed strains and the experimental measurements to perform damage identification in a structural health monitoring framework. However, only damage detection and localization are performed, without attempting a proper damage size estimation. The latter could be based on machine learning techniques; however, an a priori definition of the damage conditions would be required. To overcome these limitations, the present work proposes a new approach in which the damage is systematically introduced in the iFEM model to minimize its discrepancy with respect to the physical structure. This is performed with a maximum likelihood estimation framework, where the most accurate damage scenario is selected among a series of different models. The proposed approach was experimentally verified on an aluminum plate subjected to fatigue crack propagation, which enables the creation of a digital twin of the structure itself. The strain field fed to the iFEM routine was experimentally measured with an optical backscatter reflectometry fiber and the methodology was validated with independent observations of lasers and the digital image correlation

PREDICTION OF PROCESS-INDUCED DEFORMATIONS USING DEEP LEARNING INTERFACED FINITE ELEMENT (FE) CONSTITUTIVE MODELS

The aim of the study is to improve the predictive capacity of a Finite Element tool in relation to a rheological thermo-chemo-viscoelastic constitutive model. This enhancement specifically focuses on accurately capturing the Process Induced Deformations (PID) resulting from the polymerization of thermoset composite matrix. These deformations are due to the internal residual stress that arises from the material's inherent anisotropic properties, specifically the coefficients of thermal expansion and chemical shrinkage. The focus of the study is to accurately model the cure polymerization behaviour, which is known to have a significant impact on manufacturing defects. To account for the effect of process variables, such as maximum curing temperatures and temperature rates, a non-parametric neural network model is implemented instead of a parametric diffusion cure-kinetics model. Such model is trained using Differential Scanning Calorimetry characterization tests and is interfaced with the classical visco-elastic constitutive model to predict the evolution of thermoset resin states, which is evaluated using two cure state variables: degree of cure and glass transition temperature. This improved prediction of state transitions results in precise evaluations of internal residual stresses, leading to accurate PID predictions. Anisotropic properties of carbon/epoxy woven composite at different states of cure are used for the numerical analyses. Finally, the enhanced methodology is applied to a case study of a Z-shaped thermoset part, and the predicted PID closely associates with the experimental measures

A method for determining the distribution of carbon nanotubes in nanocomposites by electric conductivity

Carbon nanotube (CNT) polymer nanocomposites are one of the most promising materials due to their remarkable mechanical properties as well as the electrical conductivity, which offers the capability of monitoring the deformation and damage of composite structures by measuring the related conductivity variations. However, quantifying the distribution of CNTs inside the material remains a challenge with respects to both experimental and numerical works. In the current study, the electrical conductivity was used to determine the microstructure of CNT-reinforced polymer. By introducing a modified parameter related to the polar angle of CNTs, the mechanical properties as well as the electrical conductivity change with respect to deformation of nanocomposites can be replicated. After validation by experimental data from the multi-walled CNT/polymer nanocompo sites under tensile loading, the capability of the current method was then studied for composites with various weight fractions of nanotubes. (C) 2022 The Authors. Published by Elsevier B.V

Particle filter-based damage prognosis using online feature fusion and selection

Damage prognosis generally resorts to damage quantification functions and evolution models to quantify the current damage state and to predict the future states and the remaining useful life (RUL). The former typically consists of a function describing the relationship between the damage state and a statistical feature extracted from the measured signals, thus the prognostic performance will strongly depend on the selection of a proper feature. Given the best feature may vary for different specimens or even at each time instant for the same specimen during damage progression, such selection is a challenging task but has received little investigation so far. In this context, this paper proposes a particle filter-based damage prognosis framework, which involves an online feature fusion and selection scheme. A prognostic model is considered for each feature, with a multivariate process equation, formulated using both a damage degradation function and a bias parameter, and a measurement equation linking the damage state and that feature considering a data-driven model and the bias. One PF is used to estimate the damage state, its evolution parameters, and the bias for each model. Then, at each step, the feature with the smallest estimated bias is selected as the best feature providing the most likely state vectors and is used to select the most likely samples of the damage state and growth parameters for predicting the RUL and for calculating the prior at the next step. The proposed prognostic framework is demonstrated by an experimental study, where an aluminum lug structure subject to fatigue crack growth is monitored by a Lamb wave measurement system

Particle filter-based delamination shape prediction in composites subjected to fatigue loading

Modeling generic size features of delamination, such as area or length, has long been considered in the literature for damage prognosis in composites through specific models describing damage state evolution with load cycles or time. However, the delamination shape has never been considered, despite that it holds important information for damage diagnosis and prognosis, including the delamination area, its center, and perimeter, useful for structural safety evaluation. In this context, this paper develops a novel particle filter (PF)-based framework for delamination shape prediction. To this end, the delamination image is discretized by a mesh, where control points are defined as intersections between the grid lines and the perimeter of the delamination. A parametric data-driven function maps each point position as a function of the load cycles and is initially fitted on a sample test. Then, a PF is independently implemented for each node whereby to predict their future positions along the mesh lines, thus allowing delamination shape progression estimates. The new framework is demonstrated with reference to experimental tests of fatigue delamination growth in composite panels with ultrasonics C-scan monitoring

On the performance of a cointegration-based approach for novelty detection in realistic fatigue crack growth scenarios

Confounding influences, such as operational and environmental variations, represent a limitation to the implementation of Structural Health Monitoring (SHM) systems in real structures, potentially leading to damage misclassifications. In this framework, this study considers cointegration as a state of the art method for data normalisation in fatigue crack propagation scenarios, where monitoring is performed by a distributed network of strain sensors. Specifically, the work is aimed at demonstrating the effectiveness of cointegration on real engineering data in a new context, where the damage is continuously growing. Cointegration is applied at first in a controlled scenario consisting of a numerical strain simulation by means of a finite element model, modified in order to take realistic temperature fluctuations and sensor noise into account. Afterwards, detrending and anomaly detection performances are verified in two different experimental programmes on realistic aeronautical structures subjected to fatigue crack growth, including a full-scale fatigue test on a helicopter tail boom. Strain measurements are taken from a network of Fibre Bragg Grating (FBG) sensors, known to be extremely sensitive to temperature variations, hence delivering challenging scenarios for cointegration testing. Results are shown to be in good agreement with the experimental evidence, with the cointegration algorithm capable of detecting the onset of damage propagation within a 4 mm increment from a baseline condition

Searching for supergiant fast X-ray transients with Swift

Supergiant fast X-ray transients (SFXTs) are high mass X-ray binaries (HMXBs) hosting a neutron star and an OB supergiant companion. We examine the available Swift data, as well as other new or archival/serendipitous data, on three sources: IGR J17407-2808, 2XMM J185114.3-000004, and IGR J18175-2419, whose X-ray characteristics qualify them as candidate SFXT, in order to explore their properties and test whether they are consistent with an SFXT nature. As IGR J17407-2808 and 2XMM J185114.3-000004 triggered the Burst Alert Telescope on board Swift, the Swift data allow us to provide their first arcsecond localisations, leading to an unequivocal identification of the source CXOU J174042.0-280724 as the soft X-ray counterpart of IGR J17407-2808, as well as their first broadband spectra, which can be fit with models generally describing accreting neutron stars in HMXBs. While still lacking optical spectroscopy to assess the spectral type of the companion, we propose 2XMM J185114.3-000004 as a very strong SFXT candidate. The nature of IGR J17407-2808 remains, instead, more uncertain. Its broad band properties cannot exclude that the emission originates from either a HMXB (and in that case, a SFXT) or, more likely, a low mass X-ray binary. Finally, based on the deep non-detection in our XRT monitoring campaign and a careful reanalysis of the original Integral data in which the discovery of the source was first reported, we show that IGR J18175-2419 is likely a spurious detection.Comment: Accepted for publication in Astronomy and Astrophysics. 12 pages, 11 figures, 6 table

On a meta-learning population-based approach to damage prognosis

The current work studies the application of population-based structural health monitoring (PBSHM) to the problem of damage prognosis. Two methods are proposed for population-informed damage prognosis and they are evaluated according to their performance using an experimental dataset. The first method is an attempt to define a functional subspace, which includes the potential behaviour of members of the population subjected to the phenomenon of damage evolution. The second approach is a meta-learning method, the deep kernel transfer (DKT) method, which seeks to exploit information from a population in order to enhance the predictive performance of a Gaussian process. The predictive capabilities of the two methods are tested in an experimental crack-growth problem. The results reveal that the two methods are properly informed by the population to make predictions about new structures and show potential in dealing with the problem of damage evolution, which is a problem of imbalanced and difficult-to-acquire data

Redshift Limits of BL Lacertae Objects from Optical Spectroscopy

Context: BL Lacertae objects have been the targets for numerous recent multiwavelength campaigns, continuum spectral variability studies, and theoretical spectral and variability modeling. A meaningful interpretation of the results of such studies requires a reliable knowledge of the objects' redshifts; however, the redshifts for many are still unknown or uncertain. Aims: Therefore, we hope to determine or constrain the redshifts of six BL Lac objects with unknown or poorly known redshifts. Methods: Observations were made of these objects with the MDM 2.4 m Hiltner telescope. Although no spectral features were detected, and thus no redshifts could be measured, lower redshift limits were assigned to the objects based on the expected equivalent widths of absorption features in their host galaxies. Redshifts were also estimated for some objects by assuming the host galaxies are standard candles and using host galaxy apparent magnitudes taken from the literature. Results: The commonly used redshift of $z=0.102$ for 1219+285 is almost certainly wrong, while the redshifts of the other objects studied remain undetermined.Comment: 4 pages, 2 figures. Accepted by A&A Research Note