Aircraft Structural Health Monitoring Research Articles

A high-performance, sensitive, low-cost LIG/PDMS strain sensor for impact damage monitoring and localization in composite structures

Health monitoring of composite structures in aircraft is critical, as these structures are commonly utilized in weight-sensitive areas and innovative designs that directly impact flight safety and reliability. Traditional monitoring methods have limitations in monitoring area, strain limit, and signal processing. In this paper, a multifunctional sensor has been developed using acid-treated laser-induced graphene (A-LIG) with a multi-layer three-dimensional conductive network. Compared to untreated laser-induced graphene, the sensitivity of A-LIG sensor is increased by 100%. Furthermore, PDMS is used to fill the pores, which improves the fatigue performance of the A-LIG sensor. To obtain clear monitoring results, a data conversion algorithm is provided to convert the electrical signal obtained by the sensor into a strain field contour cloud map. The impact test of the A-LIG/PDMS sensor on the carbon fiber panel of the aircraft wing box segment verifies the effectiveness of its strain sensing. This work introduces a novel approach to fabricating flexible sensors with improved sensitivity, extended strain range, and cost-effectiveness. The sensor exhibits high sensitivity (gauge factor, GF ≈ 387), is low hysteresis (∼53 ms), and has a wide working range (up to 47%), and a highly stable and reproducible response over multiple test cycles (>18 000) with good switching response. It presents a promising and innovative direction for utilizing flexible sensors in the field of aircraft structural health monitoring.

Structural health monitoring of aircraft through prediction of delamination using machine learning.

Structural health monitoring (SHM) is a regular procedure of monitoring and recognizing changes in the material and geometric qualities of aircraft structures, bridges, buildings, and so on. The structural health of an airplane is more important in aerospace manufacturing and design. Inadequate structural health monitoring causes catastrophic breakdowns, and the resulting damage is costly. There is a need for an automated SHM technique that monitors and reports structural health effectively. The dataset utilized in our suggested study achieved a 0.95 R2 score earlier. The suggested work employs support vector machine (SVM) + extra tree + gradient boost + AdaBoost + decision tree approaches in an effort to improve performance in the delamination prediction process in aircraft construction. The stacking ensemble method outperformed all the technique with 0.975 R2 and 0.023 RMSE for old coupon and 0.928 R2 and 0.053 RMSE for new coupon. It shown the increase in R2 and decrease in root mean square error (RMSE).

A multi-layer ML model evolutionary paradigm for high-accuracy individual aircraft SHM

Evolutionary Machine Learning (EML) has emerged as a novel direction for providing robust methodologies for effectively tackling complex problems. However, most current research focuses on multi-objective optimization problems in machine learning (ML). There is limited research on the EML mechanism along the temporal application process, where a fixed machine learning model trained with the prior dataset will inevitably be not representative of the target problem with time. Taking the Structural Health Monitoring (SHM)-ML damage diagnosis model as an example, this paper proposes a novel multi-layer ML model evolutionary paradigm with multi-source data for high-accuracy individual aircraft SHM. The proposed paradigm considers three interconnected layers for the SHM-ML damage diagnosis model, including the dataset to establish the diagnosis model, the model structure and its parameter training, and mapping relationship used in the model to give the damage size. Besides, for a specific model of aircraft, there exist multiple stages from design to service, where multi-source of data is fused for the multi-layer evolution, enabling continuous improvement of damage diagnosis accuracy. Experimental validation is conducted on critical connection structures of aircraft, specifically the attachment lug structure, using the active guided wave SHM method. The results demonstrate the effectiveness of the proposed multi-layer ML model evolutionary paradigm.

Bonding quality monitoring of Carbon Fiber Reinforced Plastics bonded structures by fiber Bragg gratings

This paper deals with a new method for the continuous and remote monitoring of the bonding quality of Carbon Fiber Reinforced Plastics (CFRP) bonded structures. The proposed approach is based on the use of Fiber Bragg Gratings (FBGs) sensors and relies on the dependence of their temperature-induced strain sensitivity, generated by local inductions of (even small) thermal changes, on the quality of the bonding. Bonding composite laminates is a structural solution often adopted in aeronautics. The presence of disbonded areas during production is normally investigated with nondestructive tests (NDT), generally based on ultrasonic techniques (UT). However, these techniques do not always allow to detect areas with poor bonding quality (insufficient adhesion forces). To simulate such situations, some specimens were used for the experimental tests consisting of CFRP flat skins partially overlapped in a bonded area. To reproduce various bonding conditions, the specimens were bonded with different adhesive laying techniques. Experimental results here reported demonstrate that the FBGs thermal sensitivity can change significantly depending on the quality of the bonding in proximity of their position and can increase up to ∼ 50 % if the FBG is positioned in proximity of areas of the composite laminates presenting a disbonding or defects. The proposed monitoring approach is useful for in-service structural health monitoring of aircraft, supporting the NDT methods usually included in maintenance manuals.

Adaptive guided wave-based damage identification under unknown load conditions

Damage identification methods based on guided waves (GWs) have been widely researched in the field of aircraft structural health monitoring. Notably, the existing research has not extensively considered the realization of accurate damage localization in an unknown load environment, although this aspect is of significance to the real-time safety assessment of aircraft structures. To address this issue, we propose an adaptive damage imaging method based on GW signals for typical aircraft structures subjected to unknown workloads. First, through the GW signals and load data sampled in the working load range, a mathematical model between the characteristics of the GW signal and load is established. Second, the active GW signal is adaptively compensated by the real-time identified load through the passive strain signal during the service process of the structure. Finally, the alternating time-reversal phase synthesis (ATRPS) method is used to accurately locate the damage in the monitoring area. The feasibility, applicability, and accuracy of the proposed methodology are validated by three levels of experimental cases. All results consistently indicate that reliable damage localization can be achieved under an unknown load environment.

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

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.

Simulation analysis method of expandable and flexible sensor networks based on the flexible printed circuit process

Aircraft structural health monitoring (SHM) technology is a revolutionary technology based on sensor networks, which has the advantages of ensuring flight safety, extending structural life, and reducing maintenance costs. Due to the large size of aircraft structures and large deformation of morphing wings, the sensor networks integrated into the aircraft structures should be large-area, expandable, and flexible. Island-interconnect structure design in flexible electronics is expected to realize such sensor networks, with the help of large-area and low-cost flexible printed circuit (FPC) process. Mechanical analysis can guide the design of island-interconnect sensor networks. However, few studies have been reported to analyze the mechanical properties of whole island-interconnect sensor networks. This paper proposes a simulation analysis method based on the finite element method for the mechanical analysis of expandable and flexible sensor networks. This method mainly includes two parts: extracting the buckling modes through linear buckling analysis and obtaining the deformation states and mechanical properties through postbuckling behavior analysis. It is applied on the mechanical analysis of island-interconnect sensor networks with different geometry designs, including serpentine, fractal, and irregular island-interconnect sensor networks. Tensile experiments of island-interconnect sensor networks manufactured by the FPC process show that the deformation of experimental results is basically consistent with that of simulation results. In conclusion, the proposed simulation analysis method is appliable for the mechanical analysis of serpentine, fractal, and irregular island-interconnect structure networks, guiding the structural design of expandable and flexible sensor networks for large-area SHM of aircraft.

Structural health monitoring for light aircraft

The paper deals with the problem of light aircraft structural health monitoring. The phenomenological basis and engineering decisions for methods to monitor accumulated fatigue damage and to register overstress of aircraft principal structural elements have been shown. The proposed fatigue indicator is lightweight and autonomous; the output information of the indicator reflects the inhering feature of the fatigue damage – appearance and growth of surface extrusions and intrusions. The overstress indicator simplicity and reliability of the action is provided by “fuse” principle of the design.

Ways to Determine the Structural Resource of Highly Maneuverable Aircraft

This paper emphasizes the importance of monitoring aircraft structural fatigue and proposes as paramount the analysis of military aircraft structural constitutive materials aging process, during flight. Aircraft structural health monitoring is defined as an evaluation process for the integrity of key structural components with the sole purpose of enhancing flight safety and mitigating maintenance risks and loses. Nowadays, aircraft are predominantly built from composite materials with at least two layers with different mechanical properties, which combined, result in a new composite material with highly superior resilience and elastic properties. Even though there are great advantages of using composite materials, when it comes to operating military aircraft, a mere visual inspection cannot be sufficient for observing or identifying structural damages within the composite layers of the aircraft. The focus of this study is on improving the process of monitoring the structural condition of highly maneuverable military jet aircraft by using the strain gauge marks methods. Monitoring aircraft structural condition can be easily implemented, both during maintenance and flight missions, by using a wide array of sensors that can generate specific sets of instructions for all processes involved in enhancing flight and maintenance safety. The main concern of implementing a structural evaluation, within aircraft maintenance processes, sits on preventing and not ameliorating incidents and accidents during flight. As a result of the continuous research and testing, it has been proved that the operational resource of the aircraft structure, provided by the manufacturer, can be easily reached in a lot lesser flight hours than initially anticipated, by engaging the aircraft in specific operational missions and, therefore, inflicting pronounced fatigue on the aircraft structure.

Modeling of an aircraft structural health monitoring sensor network for operational impact assessment

Structural health monitoring (SHM) of aircraft components can improve maintenance operations, potentially reducing costs for inspections, unscheduled maintenance events, and unexpected delays. On the other hand, aircraft safety and net present value can be adversely influenced by false alarms, missed detections, system costs, and weight and power requirements of the SHM system. In order to gain a better understanding into the latter, we present a weight and power model for a sensor network, comprising sensors, interrogators, data collectors, and wiring. We assess the net benefit of using SHM in terms of reduced expenditure as function of network coverage, considering a corresponding potential in reducing the inspection effort.

High-Temperature and Flexible Piezoelectric Sensors for Lamb-Wave-Based Structural Health Monitoring.

Piezoelectric sensors can be utilized in Lamb-wave-based structural health monitoring (SHM), which is an effective method for aircraft structural damage detection. However, due to the inherent stiffness, brittleness, weight, and thickness of piezoelectric ceramics, their applications in aircraft structures with complex curved surfaces are seriously restricted. Herein, we report a flexible, light-weight, and high-performance BaTiO3:Sm2O3/SrRuO3/SrTiO3/mica film sensor that can be used in high-temperature SHM of aircraft. Enhanced ferroelectric Curie temperature (487 °C) and piezoelectric coefficient d33 (120-130 pm/V) are achieved in BaTiO3, which can be attributed to the tensile strain developed by stiff Sm2O3 nanopillars. Stable ferroelectricity and piezoelectricity are retained up to 150 °C. The flexible BaTiO3:Sm2O3/SrRuO3/SrTiO3/mica film is validated as an ultrasonic sensor with high sensitivity and stability for damage monitoring on aircraft structures with the curved surface ranging from 25 to 150 °C. Our work demonstrates that flexible and light-weight BaTiO3:Sm2O3/SrRuO3/SrTiO3/mica film sensors can be employed as high-temperature piezoelectric sensors for real-time SHM of aircraft structures with complex curved surfaces.

A Local TR-MUSIC Algorithm for Damage Imaging of Aircraft Structures.

Lamb wave-based damage imaging is a promising technique for aircraft structural health monitoring, as enhancing the resolution of damage detection is a persistent challenge. In this paper, a damage imaging technique based on the Time Reversal-MUltiple SIgnal Classification (TR-MUSIC) algorithm is developed to detect damage in plate-type structures. In the TR-MUSIC algorithm, a transfer matrix is first established by exciting and sensing signals. A TR operator is constructed for eigenvalue decomposition to divide the data space into signal and noise subspaces. The structural space spectrum of the algorithm is calculated based on the orthogonality of the two subspaces. A local TR-MUSIC algorithm is proposed to enhance the image quality of multiple damages by using a moving time window to establish the local space spectrum at different times or different distances. The multidamage detection capability of the proposed enhanced TR-MUSIC algorithm is verified by simulations and experiments. The results reveal that the local TR-MUSIC algorithm can not only effectively detect multiple damages in plate-type structures with good image quality but also has a superresolution ability for detecting damage with distances smaller than half the wavelength.

Heteroepitaxy of flexible piezoelectric Pb(Zr0.53Ti0.47)O3 sensor on inorganic mica substrate for lamb wave-based structural health monitoring

Active Lamb wave detection is an effective and simple monitoring method for structural health monitoring (SHM). Piezoelectric lead zirconate titanate (PZT) ceramics are most commonly used as transducers to excite and receive Lamb wave. However, due to their hardness, thickness and brittleness, PZT ceramics are severely limited in detecting damage on curved surface structures. Herein, we report that flexible PZT film sensor is an excellent candidate for receiving Lamb wave used in Lamb wave-based SHM of aircraft. An improved piezoelectric coefficient d33 (~130 pm V−1) is obtained in epitaxial PZT films grown on flexible inorganic mica substrates via van der Waals heteroepitaxy. Superior stable ferroelectricity and piezoelectricity retain in flexible PZT film after bending 104 cycles under a radius of 8 mm. As proven by experiment and simulation, flexible PZT film is demonstrated as an ultrasonic sensor with high sensitivity to be bonded on the curved aluminum plate for Lamb wave-based damage monitoring. Our work demonstrates that flexible PZT films have enormous potential for real-time light-weight and high-sensitivity receiver sensors for the SHM of aging aircraft.

Impact and acoustic emission performance of polyvinylidene fluoride sensor embedded in glass fiber-reinforced polymer composite structure

Smart materials find vital applications in the aerospace industry due to their ability to adapt to surrounding conditions according to design requirements and applicability. Piezoelectric materials are commonly used under the category of smart materials for transducer applications. Among piezoelectric materials, piezo polymer polyvinylidene fluoride (PVDF) is widely used for structural health monitoring (SHM) applications of composite structures, acoustic emission (AE) sensor, accelerometer, strain gauge, pressure sensor, and so on because of its outstanding piezo stress constant ( g 33), piezo strain constant ( d 33), flexibility, and lightweight. In this article, glass fiber-reinforced polymer (GFRP) laminates have been prepared by embedding the PVDF sensor into GFRP for the first time. A detailed study has been done on the behavior and characterization of the PVDF sensor embedded in GFRP. The PVDF sensors embedded in laminates were subjected to impact test, where a constant weight of 5.5 kg was dropped from a height of 10–60 mm in the interval of 10 mm, and the voltage response of the PVDF sensor was recorded. Sensitivity analysis and AE test of the PVDF sensor in GFRP were also carried out. This is useful for various aerospace applications especially for SHM of aircraft.

Damage Detection and Characterization in Composites Using a Geometric Modification of the RAPID Algorithm

Structural Health Monitoring (SHM) of aircraft structures, which are manufactured more and more using composite material, is a well-established technique for increasing reliability and reducing maintenance costs. Implementing damage inspection algorithms that run in dedicated electronic systems helps reduce big data processing time with improved results. RAPID (Reconstruction Algorithm for Probabilistic Inspection of Damage) algorithm, developed some years ago, provides a technique to identify and characterize damages both in metallic and composite materials. However, this algorithm sometimes provides inaccurate predictions of the damage location, caused mainly by the influence of path intersection points among transducers, that can mask the current location of damages. This work presents a geometrical modification of standard RAPID algorithm (RAPID-G) that mitigates the influence of the intersection point between sensor paths. The results highlight that the location of damages improves significantly with this modification of the algorithm.

Dynamic probability modeling-based aircraft structural health monitoring framework under time-varying conditions: Validation in an in-flight test simulated on ground

This paper proposes a dynamic probability modeling-based aircraft Structural Health Monitoring (SHM) framework which provides a modular hierarchical SHM architecture for the development of applicable aircraft SHM techniques. The problem of reliable damage monitoring under time-varying conditions, which is the main application obstacle, is fully considered in the framework by the double probability models combined with the short term and long term dynamic update of the models. To realize the SHM capability of the framework, an adaptive constructing method of Gaussian mixture model is proposed for stable and efficient probability modeling and the probability similarity between dynamically updated models is measured for normalized damage detection. The framework is realized by combining with the guided wave SHM technique and validated in a full-scale aircraft fatigue test which is an in-flight test simulated on ground. The cracks of the right landing gear spar and the left wing panel on the aircraft structure are monitored reliably under the fatigue load conditions.

Analysis and Study of FBG Sensor in Crack Monitoring of Aircraft Structure

Based on the FBG sensing principle, the stress of each test point was measured by fatigue test with the change of tensile time using the strain sensitivity characteristics of FBG (fiber Bragg grating) sensor, and the experimental results were compared with the fatigue theory, FBG sensor can not only monitor the various stages of the crack growth, but also can effectively determine the time of occurrence of fatigue cracks and the top of the general location. This will provide a strong basis for the further application of FBG sensors in aircraft structural health monitoring.

Corrosion issues in structural health monitoring of aircraft

Last couple of decades have seen significant progress in the paradigm of structural health monitoring (SHM) which is expected to provide enhancements in the levels of safety and mission readiness of an aircraft or a fleet of aircraft at an affordable cost. These developments comprise, broadly, (1) a diagnosis part which deals with detection of structural damage through various sensing technologies, (2) a prognosis part which attempts to forecast the performance with the current damage state under anticipated future loading environments and (3) a damage mitigation strategy for dealing with the identified damage. Much of the research and developmental effort on these three fronts has focused on mechanical damage such as cracks and similar flaws in metals and impact damage and delaminations in composites and on their growth under operational loads through processes such as fatigue. Relatively less attention has been devoted to problems of metal corrosion, which has been also a bothersome factor in ensuring structural integrity of airframe structures. This paper makes an attempt to highlight some of the issues that are being faced or may be faced while bringing in corrosion into the SHM framework for aircraft. Towards this objective, a review of the current state of the art on various aspects of monitoring of corrosion damage is presented. Beginning with the phenomenon of corrosion (especially in Al alloys) to identify parameters that may be helpful for its monitoring, the review covers important aspects of corrosion damage, sensing concepts and technologies. Five categories of techniques are described: (1) direct monitoring of corrosion damage, (2) monitoring the state of corrosion through coupons and surrogates through physical means, (3) electrochemical techniques, (4) monitoring the corrosivity of the environment as an indirect assessment of corrosion and (5) monitoring the degradation in corrosion prevention coatings as a means to infer underlying corrosion. A special subsection is devoted to use of optical fibres in corrosion sensing. Instances of actually using all such sensors in aircraft are briefly mentioned. An overall discussion of the current state of the art is provided. It is emphasised that corrosion sensing and monitoring are increasingly using developments in “smart technology”, such as smart coatings, thin-film technology, smart materials such as magneto-resistive and magneto-optical, smart polymers and microdevices fabrication. It is expected that the issues highlighted here will be of relevance not only for ageing aircraft but also for new aircraft under design or in operation.

An energy-efficient cyber-physical system for wireless on-board aircraft structural health monitoring

In this paper, an energy-efficient cyber-physical system using piezoelectric transducers (PZTs) and wireless sensor networks (WSN) is proposed, designed and experimentally validated for on-board aircraft structural health monitoring (SHM). A WSN is exploited to coordinate damage detection using PZTs distributed on the whole aircraft. An active sensing methodology is adopted for PZTs to evaluate the structural integrity in a pitch-catch manner. The system configuration and operation principle are discussed in the first place. Then, the detailed hardware design was introduced. The proposed system is not only characterized as low-power, high-compactness and wireless, but also capable of processing actuating-sensing signals at megahertz, generating actuating signals with great flexibility, handling multiple actuating-sensing channels with marginal crosstalk. The design was implemented on a 4-layer printed circuit board (8 × 6.5 cm) and evaluated on a large-scale composite fuselage. A 5 MHz sampling rate for actuating and 1.8 MHz for sensing (8 channels) were realized, and the accuracy was validated by comparing the results with those from an oscilloscope. The crosstalk issue caused by actuation on sensing channels is properly addressed using a 2-stage attenuation method. An ultra-low current (81.7 μA) was measured when no detection was required; the average current was 0.45 mA with a detection rate of twice per hour, which means the system can continuously work for up to 12.6 months for 2 AA batteries. Eventually, an example of damage detection is provided, showing the capability of such a system in SHM.

Crack Monitoring for Hot-Spot Areas Under Time-Varying Load Condition Based on FCM Clustering Algorithm

Crack monitoring for damage-prone structural area, named hot-spot, is one of the most interesting concerned technologies for aircraft structural health monitoring. This paper proposes an efficient technique based on Fuzzy c-means (FCM) clustering algorithm to monitor the crack growth under time-varying load condition. The proposed method first extracts damage indexes from the collected signals to construct the baseline model as well as the online FCM model. Migration index of the online model is calculated by Kullback-Liebler Divergence (KLD) and Bhattacharyya Distance (BD), respectively. By comparing with the Gaussian mixture model (GMM), experimental results show that the proposed FCM algorithm has less computation time and its migration index curve is smoother when the crack size is small. Furthermore, KLD achieves a higher sensitivity than BD as crack grows.