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Damage Detection Systems for Aerospace Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 56226

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Special Issue Editor

Prof. Dr. Rhys Pullin

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Guest Editor

Department for Mechanical Engineering, Cardiff University, Cardiff CF24 3AA, UK
Interests: acoustic emission; aerospace structures; structural health monitoring

Special Issue Information

The aerospace industry is aiming for a cleaner means of transport. One way to achieve this is by making transportation lighter, thus directly improving fuel efficiency and reducing environmental impact. A further aim of the industry is to reduce maintenance time in order to reduce operating costs, which can result in a reduction of air transport cost, benefitting both passenger and freight services. Current developments to support these aims include using advanced materials, with the current generation of aerospace structures being 50% composite materials. These materials offer a weight reduction whilst maintaining adequate stiffness; however, their damage mechanics is very complex and less deterministic than that of metals. This results in an overall reduced benefit. Structures are manufactured to be thicker using additional material in order to accommodate unknown or unpredictable failure modes, which cannot be easily detected during maintenance. A way to overcome these issues is the adoption of a structural health monitoring (SHM) inspection system utilizing energy harvesting and a wireless sensor network. Such systems obtain information about the status of a structure in real time, which can feed back into an asset management framework, to determine maintenance schedules and allow for lighter structures to be designed whilst increasing safety. This Special Issue aims to collate the latest advancements in this key active research area.

Dr. Rhys Pullin
Guest Editor

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Keywords

damage detection
energy harvesting
optimization
wireless communication
data management

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Published Papers (10 papers)

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Research

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16 pages, 2350 KiB

Open AccessArticle

Characterization of Biocomposites and Glass Fiber Epoxy Composites Based on Acoustic Emission Signals, Deep Feature Extraction, and Machine Learning

by Tomaž Kek, Primož Potočnik, Martin Misson, Zoran Bergant, Mario Sorgente, Edvard Govekar and Roman Šturm

Sensors 2022, 22(18), 6886; https://doi.org/10.3390/s22186886 - 13 Sep 2022

Cited by 4 | Viewed by 2368

Abstract

This study presents the results of acoustic emission (AE) measurements and characterization in the loading of biocomposites at room and low temperatures that can be observed in the aviation industry. The fiber optic sensors (FOS) that can outperform electrical sensors in challenging operational environments were used. Standard features were extracted from AE measurements, and a convolutional autoencoder (CAE) was applied to extract deep features from AE signals. Different machine learning methods including discriminant analysis (DA), neural networks (NN), and extreme learning machines (ELM) were used for the construction of classifiers. The analysis is focused on the classification of extracted AE features to classify the source material, to evaluate the predictive importance of extracted features, and to evaluate the ability of used FOS for the evaluation of material behavior under challenging low-temperature environments. The results show the robustness of different CAE configurations for deep feature extraction. The combination of classic and deep features always significantly improves classification accuracy. The best classification accuracy (80.9%) was achieved with a neural network model and generally, more complex nonlinear models (NN, ELM) outperform simple models (DA). In all the considered models, the selected combined features always contain both classic and deep features. Full article

(This article belongs to the Special Issue Damage Detection Systems for Aerospace Applications)

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19 pages, 6960 KiB

Open AccessArticle

High-Speed and High-Temperature Calorimetric Solid-State Thermal Mass Flow Sensor for Aerospace Application: A Sensitivity Analysis

by Lucas Ribeiro, Osamu Saotome, Roberto d’Amore and Roana de Oliveira Hansen

Sensors 2022, 22(9), 3484; https://doi.org/10.3390/s22093484 - 3 May 2022

Cited by 9 | Viewed by 2859

Abstract

A high-speed and high-temperature calorimetric solid-state thermal mass flow sensor (TMFS) design was proposed and its sensitivity to temperature and airflow speed were numerically assessed. The sensor operates at 573.15 Kelvin (300 °C), measuring speeds up to 265 m/s, and is customized to be a transducer for an aircraft Air Data System (ADS). The aim was to enhance the system reliability against ice accretion on pitot tubes’ pressure intakes, which causes the system to be inoperative and the aircraft to lose protections that ensure its safe operation. In this paper, the authors assess how the distance between heater and thermal sensors affects the overall TMFS sensitivity and how it can benefit from the inclusion of a thermal barrier between these elements. The results show that, by increasing the distance between the heater and temperature sensors from 0.1 to 0.6 mm, the sensitivity to temperature variation is improved by up to 80%, and that to airspeed variation is improved by up to 100%. In addition, adding a thermal barrier made of Parylene-N improves it even further, by nearly 6 times, for both temperature and air speed variations. Full article

(This article belongs to the Special Issue Damage Detection Systems for Aerospace Applications)

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22 pages, 18811 KiB

Open AccessArticle

An Assessment of the Effect of Progressive Water Absorption on the Interlaminar Strength of Unidirectional Carbon/Epoxy Composites Using Acoustic Emission

by Faisel Almudaihesh, Stephen Grigg, Karen Holford, Rhys Pullin and Mark Eaton

Sensors 2021, 21(13), 4351; https://doi.org/10.3390/s21134351 - 25 Jun 2021

Cited by 6 | Viewed by 2959

Abstract

Carbon Fibre-Reinforced Polymers (CFRPs) in aerospace applications are expected to operate in moist environments where carbon fibres have high resistance to water absorption; however, polymers do not. To develop a truly optimised structure, it is important to understand this degradation process. This study aims to expand the understanding of the role of water absorption on fibrous/polymeric structures, particularly in a matrix-dominant property, namely interlaminar strength. This work used Acoustic Emission (AE), which could be integrated into any Structural Health Monitoring System for aerospace applications, optical strain measurements, and microscopy to provide an assessment of the gradual change in failure mechanisms due to the degradation of a polymer’s structure with increasing water absorption. CFRP specimens were immersed in purified water and kept at a constant temperature of 90 °C for 3, 9, 24 and 43 days. The resulting interlaminar strength was investigated through short-beam strength (SBS) testing. The SBS values decreased as immersion times were increased; the decrease was significant at longer immersion times (up to 24.47%). Failures evolved with increased immersion times, leading to a greater number of delaminations and more intralaminar cracking. Failure modes, such as crushing and multiple delaminations, were observed at longer immersion times, particularly after 24 and 43 days, where a pure interlaminar shear failure did not occur. The observed transition in failure mechanism showed that failure of aged specimens was triggered by a crushing of the upper surface plies leading to progressive delamination at multiple ply interfaces in the upper half of the specimen. The crushing occurred at a load below that required to initiate a pure shear failure and hence represents an under prediction of the true SBS of the sample. This is a common test used to assess environmental degradation of composites and these results show that conservative knockdown factors may be used in design. AE was able to distinguish different material behaviours prior to final fracture for unaged and aged specimens suggesting that it can be integrated into an aerospace asset management system. AE results were validated using optical measurements and microscopy. Full article

(This article belongs to the Special Issue Damage Detection Systems for Aerospace Applications)

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19 pages, 3788 KiB

Open AccessArticle

Composite Laminate Delamination Detection Using Transient Thermal Conduction Profiles and Machine Learning Based Data Analysis

by David I. Gillespie, Andrew W. Hamilton, Robert C. Atkinson, Xavier Bellekens, Craig Michie, Ivan Andonovic and Christos Tachtatzis

Sensors 2020, 20(24), 7227; https://doi.org/10.3390/s20247227 - 17 Dec 2020

Cited by 5 | Viewed by 2771

Abstract

Delaminations within aerospace composites are of particular concern, presenting within composite laminate structures without visible surface indications. Transmission based thermography techniques using contact temperature sensors and surface mounted heat sources are able to detect reductions in thermal conductivity and in turn impact damage and large disbonds can be detected. However delaminations between Carbon Fibre Reinforced Polymer (CFRP) plies are not immediately discoverable using the technique. The use of transient thermal conduction profiles induced from zonal heating of a CFRP laminate to ascertain inter-laminate differences has been demonstrated and the paper builds on this method further by investigating the impact of inter laminate inclusions, in the form of delaminations, to the transient thermal conduction profile of multi-ply bi-axial CFRP laminates. Results demonstrate that as the distance between centre of the heat source and delamination increase, whilst maintaining the delamination within the heated area, the resultant transient thermal conduction profile is measurably different to that of a homogeneous region at the same distance. The method utilises a supervised Support Vector Classification (SVC) algorithm to detect delaminations using temperature data from either the edge of the defect or the centre during a 140 s ramped heating period to 80

^{°}

C. An F1 score in the classification of delaminations or no delamination at an overall accuracy of over 99% in both training and with test data separate from the training process has been achieved using data points effected by transient thermal conduction due to structural dissipation at 56.25 mm. Full article

(This article belongs to the Special Issue Damage Detection Systems for Aerospace Applications)

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24 pages, 42326 KiB

Open AccessArticle

Dynamic Characteristics and Damage Detection of a Metallic Thermal Protection System Panel Using a Three-Dimensional Point Tracking Method and a Modal Assurance Criterion

by Vinh Tung Le and Nam Seo Goo

Sensors 2020, 20(24), 7185; https://doi.org/10.3390/s20247185 - 15 Dec 2020

Cited by 10 | Viewed by 3490

Abstract

A thermal protection system (TPS) is designed and fabricated to protect a hypersonic vehicle from extreme conditions. Good condition of the TPS panels is necessary for the next flight mission. A loose bolted joint is a crucial defect in a metallic TPS panel. This study introduces an experimental method to investigate the dynamic characteristics and state of health of a metallic TPS panel through an operational modal analysis (OMA). Experimental investigations were implemented under free-free supports to account for a healthy state, the insulation effect, and fastener failures. The dynamic deformations resulted from an impulse force were measured using a non-contact three-dimensional point tracking (3DPT) method. Using changes in natural frequencies, the damping ratio, and operational deflection shapes (ODSs) due to the TPS failure, we were able to detect loose bolted joints. Moreover, we also developed an in-house program based on a modal assurance criterion (MAC) to detect the state of damage of test structures. In a damage state, such as a loose bolted joint, the stiffness of the TPS panel was reduced, which resulted in changes in the natural frequency and the damping ratio. The calculated MAC values were less than one, which pointed out possible damage in the test TPS panels. Our results also demonstrated that a combination of the 3DPT-based OMA method and the MAC achieved good robustness and sufficient accuracy in damage identification for complex aerospace structures such as TPS structures. Full article

(This article belongs to the Special Issue Damage Detection Systems for Aerospace Applications)

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17 pages, 5812 KiB

Open AccessArticle

CNN Training with Twenty Samples for Crack Detection via Data Augmentation

by Zirui Wang, Jingjing Yang, Haonan Jiang and Xueling Fan

Sensors 2020, 20(17), 4849; https://doi.org/10.3390/s20174849 - 27 Aug 2020

Cited by 27 | Viewed by 5770

Abstract

The excellent generalization ability of deep learning methods, e.g., convolutional neural networks (CNNs), depends on a large amount of training data, which is difficult to obtain in industrial practices. Data augmentation is regarded commonly as an effective strategy to address this problem. In this paper, we attempt to construct a crack detector based on CNN with twenty images via a two-stage data augmentation method. In detail, nine data augmentation methods are compared for crack detection in the model training, respectively. As a result, the rotation method outperforms these methods for augmentation, and by an in-depth exploration of the rotation method, the performance of the detector is further improved. Furthermore, data augmentation is also applied in the inference process to improve the recall of trained models. The identical object has more chances to be detected in the series of augmented images. This trick is essentially a performance–resource trade-off. For more improvement with limited resources, the greedy algorithm is adopted for searching a better combination of data augmentation. The results show that the crack detectors trained on the small dataset are significantly improved via the proposed two-stage data augmentation. Specifically, using 20 images for training, recall in detecting the cracks achieves 96% and

F_{e x t}^{(0.8)}

, which is a variant of F-score for crack detection, achieves 91.18%. Full article

(This article belongs to the Special Issue Damage Detection Systems for Aerospace Applications)

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Review

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57 pages, 21474 KiB

Open AccessReview

Energy Harvesting Technologies for Structural Health Monitoring of Airplane Components—A Review

by Saša Zelenika, Zdenek Hadas, Sebastian Bader, Thomas Becker, Petar Gljušćić, Jiri Hlinka, Ludek Janak, Ervin Kamenar, Filip Ksica, Theodora Kyratsi, Loucas Louca, Miroslav Mrlik, Adnan Osmanović, Vikram Pakrashi, Ondrej Rubes, Oldřich Ševeček, José P.B. Silva, Pavel Tofel, Bojan Trkulja, Runar Unnthorsson, Jasmin Velagić and Željko Vrcan Show full author list Hide full author list

Sensors 2020, 20(22), 6685; https://doi.org/10.3390/s20226685 - 22 Nov 2020

Cited by 59 | Viewed by 12532

Abstract

With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 “Optimising Design for Inspection” (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components. Full article

(This article belongs to the Special Issue Damage Detection Systems for Aerospace Applications)

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27 pages, 2418 KiB

Open AccessReview

Development and Application of Infrared Thermography Non-Destructive Testing Techniques

by Zhi Qu, Peng Jiang and Weixu Zhang

Sensors 2020, 20(14), 3851; https://doi.org/10.3390/s20143851 - 10 Jul 2020

Cited by 91 | Viewed by 13605

Abstract

Effective testing of defects in various materials is an important guarantee to ensure its safety performance. Compared with traditional non-destructive testing (NDT) methods, infrared thermography is a new NDT technique which has developed rapidly in recent years. Its core technologies include thermal excitation and infrared image processing. In this paper, several main infrared thermography nondestructive testing techniques are reviewed. Through the analysis and comparison of the detection principle, technical characteristics and data processing methods of these testing methods, the development of the infrared thermography nondestructive testing technique is presented. Moreover, the application and development trend are summarized. Full article

(This article belongs to the Special Issue Damage Detection Systems for Aerospace Applications)

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Other

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13 pages, 3493 KiB

Open AccessLetter

Identifying Defects in Aerospace Composite Sandwich Panels Using High-Definition Distributed Optical Fibre Sensors

by James A. Mills, Andrew W. Hamilton, David I. Gillespie, Ivan Andonovic, Craig Michie, Kenneth Burnham and Christos Tachtatzis

Sensors 2020, 20(23), 6746; https://doi.org/10.3390/s20236746 - 25 Nov 2020

Cited by 9 | Viewed by 4599

Abstract

Automated methods for detecting defects within composite materials are highly desirable in the drive to increase throughput, optimise repair program effectiveness and reduce component replacement. Tap-testing has traditionally been used for detecting defects but does not provide quantitative measurements, requiring secondary techniques such as ultrasound to certify components. This paper reports on an evaluation of the use of a distributed temperature measurement system—high-definition fibre optic sensing (HD-FOS)—to identify and characterise crushed core and disbond defects in carbon fibre reinforced polymer (CFRP)-skin, aluminium-core, sandwich panels. The objective is to identify these defects in a sandwich panel by measuring the heat transfer through the panel thickness. A heater mat is used to rapidly increase the temperature of the panel with the HD-FOS sensor positioned on the top surface, measuring temperature. HD-FOS measurements are made using the Luna optical distributed sensor interrogator (ODISI) 9100 system comprising a sensor fabricated using standard single mode fibre (SMF)-20 of external diameter 250

μ

m, including the cladding. Results show that areas in which defects are present modulate thermal conductivity, resulting in a lower surface temperature. The resultant data are analysed to identify the length, width and type of defect. The non-invasive technique is amenable to application in challenging operational settings, offering high-resolution visualisation and defect classification. Full article

(This article belongs to the Special Issue Damage Detection Systems for Aerospace Applications)

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13 pages, 596 KiB

Open AccessLetter

Defect Detection in Aerospace Sandwich Composite Panels Using Conductive Thermography and Contact Sensors

by David I. Gillespie, Andrew W. Hamilton, Robert C. Atkinson, Xavier Bellekens, Craig Michie, Ivan Andonovic and Christos Tachtatzis

Sensors 2020, 20(22), 6689; https://doi.org/10.3390/s20226689 - 23 Nov 2020

Cited by 9 | Viewed by 2969

Abstract

Sandwich panels consisting of two Carbon Fibre Reinforced Polymer (CFRP) outer skins and an aluminium honeycomb core are a common structure of surfaces on commercial aircraft due to the beneficial strength–weight ratio. Mechanical defects such as a crushed honeycomb core, dis-bonds and delaminations in the outer skins and in the core occur routinely under normal use and are repaired during aerospace Maintenance, Repair and Overhaul (MRO) processes. Current practices rely heavily on manual inspection where it is possible minor defects are not identified prior to primary repair and are only addressed after initial repairs intensify the defects due to thermal expansion during high temperature curing. This paper reports on the development and characterisation of a technique based on conductive thermography implemented using an array of single point temperature sensors mounted on one surface of the panel and the concomitant induced thermal profile generated by a thermal stimulus on the opposing surface to identify such defects. Defects are classified by analysing the differential conduction of thermal energy profiles across the surface of the panel. Results indicate that crushed core and impact damage are detectable using a stepped temperature profile of 80

^{\circ}

C The method is amenable to integration within the existing drying cycle stage and reduces the costs of executing the overall process in terms of time-to-repair and manual effort. Full article

(This article belongs to the Special Issue Damage Detection Systems for Aerospace Applications)

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