Platform health management for aircraft maintenance – a review

Aircraft health management has been researched at both component and system levels. In instances of certain aircraft faults, like the Boeing 777 fuel icing problem, there is evidence suggesting that a platform approach using an Integrated Vehicle Health Management (IVHM) system could have helped detect faults and their interaction effects earlier, before they became catastrophic. This paper reviews aircraft health management from the aircraft maintenance point of view. It emphasizes the potential of a platform solution to diagnose faults, and their interaction effects, at an early stage. The paper conducts a thorough analysis of existing literature concerning maintenance and its evolution, delves into the application of Artificial Intelligence (AI) techniques in maintenance, explains the rationale behind their employment, and illustrates how AI implementation can enhance fault detection using platform sensor data. Further, it discusses how computational severity and criticality indexes (health indexes) can potentially be complementary to the use of AI for the provision of maintenance information on aircraft components, for assisting operational decisions

A comprehensive review on enhancing wind turbine applications with advanced SCADA data analytics and practical insights

The aim of this study is to explore the potential and economic benefits of utilising Supervisory Control and Data Acquisition (SCADA) data to improve wind turbine operation and maintenance activities. The review identifies a gap in the current understanding of how to effectively use SCADA data in wind turbine applications. It emphasises the need for pre-processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Additionally, it highlights the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data-driven machine learning models, and statistical regression models. The review also recognises the limitations caused by the lack of public data from wind turbine developers and the imbalance between normal operation data samples and abnormal data samples, negatively impacting model accuracy. The key findings of the review demonstrate that SCADA data-driven techniques can lead to significant improvements in wind turbine operations and maintenance. The application of data-driven technologies based on SCADA data has proven effective in reducing operation and maintenance costs and enhancing wind power generation. Moreover, the development of robust decision support systems using SCADA data minimises the need for frequent maintenance interventions in offshore wind farms. To bridge the gap and further enhance wind turbine applications using SCADA data, several recommendations are provided. These include encouraging greater openness in sharing SCADA data to improve the robustness and accuracy of AI models, adopting transfer learning techniques to overcome the scarcity of quality datasets, establishing unified standards and taxonomies, and providing specialised resources such as software applications with interactive graphical user interfaces for easier storage, annotation, and analysis of SCADA data. The authors’ review paper identifies a gap in the current understanding of how to effectively utilise SCADA data in wind turbine applications. It emphasises the importance of pre-processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Furthermore, the authors highlight the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data-driven machine learning models, and statistical regression models

Temperature monitoring of through-thickness temperature gradients in thermal barrier coatings using ultrasonic guided waves

Ultrasonic guided waves offer a promising method of monitoring the online temperature of plate-like structures in extreme environments, such as aero-engine nozzle guide vanes (NGVs), and can provide the resolution, response rate, and robust operation that is required in aerospace. Previous investigations have shown the potential of such a system but the effect of the complex physical environment on wave propagation is yet to be considered. This article uses a numerical approach to investigate how thermal barrier coatings (TBCs) applied to the surface of many components designed for extreme thermal conditions will affect ultrasonic guided wave propagation, and how a system can be employed to monitor through-thickness temperature changes. The top coat/bond coat boundary in NGVs has been shown to be a temperature critical point that is difficult to monitor with traditional temperature sensors, which highlights the potential of ultrasonic guided waves. Differences in application method and layer thickness are considered, and analysis of through-thickness displacement profiles and dispersion curves are used to predict signal response and determine the most suitable mode of operation. Heat transfer simulations (COMSOL) have been used to predict temperature gradients within a TBC, and dispersion curves have been produced from the temperature dependant material properties. Time dependant simulations of wave propagation are in good agreement with dispersion curve predictions of wave velocity for the two lowest order modes in three thicknesses of TBC top coat (100, 250, and 500 μ ). When wave velocity measurements from the simulations are compared to dispersion curves generated at isotropic temperatures, the corresponding temperature represents the average temperature of a gradient system well. Such a measurement system could, in principle, be used in conjunction with surface temperature measurement systems to monitor through-thickness temperature changes

Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm

Accurate flight delay prediction is fundamental to establishing an efficient airline business. It is considered one of the most critical intelligent aviation systems components. Recently, flight delay has been a significant cause that deprives airlines of good performance. Hence, airlines must accurately forecast flight delays and comprehend their sources to have excellent passenger experiences, increase income and minimise unwanted revenue loss. In this paper, we developed a novel approach that is an optimisation-driven deep learning model for predicting flight delays by extending a state-of-the-art method, DeepONet. We utilise the Box-Cox transformation for data conversion with a minimal error rate. Also, we employed a deep residual network for the feature fusion before training our model. Furthermore, this research uses flight on-time data for flight delay prediction. To validate our proposed model, we conducted a numerical study using the US Bureau of Transportation of Statistics. Also, we predict the flight delay by selecting the optimum weights using the novel DeepONet with the Gradient Mayfly Optimisation Algorithm (GMOA). Our experiment results show that the proposed GMOA-based DeepONet outperformed the existing methods with a Root Mean Square Error of 0.0765, Mean Square Error of 0.0058, Mean Absolute Error of 0.0049 and Mean Absolute Percent Error of 0.0043, respectively. When we apply 4-fold cross-validation, the proposed GMOA-based DeepONet outperformed the existing methods with minimal standard error. These results also show the importance of optimisation algorithms in deciding the optimal weight to improve the model performance. The efficacy of our proposed approach in predicting flight delays with minimal errors well define from all the evaluation metrics. Also, utilising the prediction outcome of our robust model to release information about the delayed flight in advance from the aviation decision systems can effectively alleviate the passengers’ nervousness.UKRI for the COVID-19 recovery grant under the budget code SA077N. This research was heavily affected by the COVID-19 pandemic during the first authors' PhD studies. This lead to an extension to registration for 3 months, which was funded by the UKRI doctoral extension recovery grant. (PTDF main funder of PhD)

Wear-resistant nickel-matrix composite coatings incorporating hard chromium carbide particles

This work evaluates the influence of plating variables on the morphology, composition homogeneity, and abrasive wear resistance of metal matrix composite coatings. A set of Ni/Cr3C2 coatings were brush plated onto steel coupons modifying two key variables: particle size and brush material. Compositional maps of unprecedented detail have been produced and analysed statistically to enhance understanding of composition distribution. The use of Abbott-Firestone curves to analyse surface morphology enabled the evaluation of valley and peak features. The coating differences highlighted by previous analyses have been compared with their behaviour in abrasive environments, simulated using Taber testing. Moreover, coupling Taber testing with partial compositional maps at different wear stages enabled monitoring of coating wear evolution. This methodology has revealed the importance of particle sedimentation during plating, which increased particle incorporation in the composite coating but also increased composition heterogeneity. The smaller 1.7 μm carbides and abrasive brushes produced coatings with more homogeneous morphologies, higher particle content, and increased resistance against abrasive wear, with a 60% reduction in material loss in comparison to the standard nickel coatings

Round-robin study for ice adhesion tests

Ice adhesion tests are widely used to assess the performance of potential icephobic surfaces and coatings. A great variety of test designs have been developed and used over the past decades due to the lack of formal standards for these types of tests. In many cases, the aim of the research was not only to determine ice adhesion values, but also to understand the key surface properties correlated to low ice adhesion surfaces. Data from different measurement techniques had low correspondence between the results: Values varied by orders of magnitude and showed different relative relationships to one another. This study sought to provide a broad comparison of ice adhesion testing approaches by conducting different ice adhesion tests with identical test surfaces. A total of 15 test facilities participated in this round-robin study, and the results of 13 partners are summarized in this paper. For the test series, ice types (impact and static) as well as test parameters were harmonized to minimize the deviations between the test setups. Our findings are presented in this paper, and the ice- and test-specific results are discussed. This study can improve our understanding of test results and support the standardization process for ice adhesion strength measurements

Autonomous Detect and Avoid algorithm respecting airborne Right of Way rules

Robust conflict resolution systems are crucial for BVLOS (beyond visual line of sight) operations of UAVs (Unmanned Aerial Vehicles) in the unsegregated airspace. The present conflict resolution research focus is skewed towards optimal path planning, often ignoring the airborne Right-of-way rules prescribed by the FAA and CAA. Although this approach might result in the most optimal path to resolve the conflict, it can cause confusion among other airspace users if the rules of the air are not obeyed when operating in the vicinity of other aircraft. In the present work, a real-time model predictive control approach is proposed that heavily prioritizes adherence to the prescribed right-of-way rules of the air. The maneuvering limitations of the involved aircraft are also taken into account. Several conflict scenarios were simulated, and the results show that the developed algorithm could resolve all conflicts

Dynamics modeling of multirotor type UAV with the blade element momentum theory and nonlinear controller design for a wind environment

This paper presents an aerodynamics modeling of a rotary type Vertical Take-Off and Landing (VTOL) aircraft using the Blade Element Momentum Theory (BEMT). The BEMT is incorporated into the rigid body dynamics to describe main force and its reactive torque by rotor of the multirotor UAV. The dynamics modeling can demonstrate the motion of the multirotor UAV in the presence of gust or wind in an unsteady environment effectively. In order to operate the multirotor UAV, a robust nonlinear control technique is designed to track the desired command and stabilize the UAV subject to uncertainties such as modeling error, external disturbances. The hierarchical Sliding Mode Control (SMC) is adopted to organize a multi-loop structure: the outer-loop and inner-loop mode. Actual control input is distributed by physical configuration of the multirotor UAV with the described thrust and torque modeling by the BEMT in the control allocation. Numerical simulation evaluates the feasibility of the dynamic modeling of the multrirotor UAV with BEMT in the presence of external wind effects, which enables to understand a motion of rotary type UAV in a windy environment

Numerical modelling of hydrogen leakages in confined spaces for domestic applications

The UK government tentatively plans to use hydrogen for domestic applications by 2035. While the use of hydrogen aims to reduce the dependence on hydrocarbons, certain factors need consideration. Since hydrogen is much lighter, and more reactive than methane, it is crucial to understand the change in risk for accident scenarios involving hydrogen in a domestic setting. Numerical modelling was used to simulate the leakage of hydrogen and methane in small, enclosed spaces such as kitchen cupboards. The k- ε turbulence model was used along with the species transport model to simulate the leakage of gas for different inlet locations and leak diameters (1.8 mm–7.2 mm). From the modelling study, it was observed that hydrogen and methane both tend to stratify from top of the control volume to the bottom. The key finding was that, under adverse conditions (leak from a 7.2 mm diameter hole) and due to greater volumetric flow, hydrogen tends to reach equilibrium concentration 45s faster than methane for a total leak duration of 600s. Additionally, it was noted that cases with leak inlet locations near corners had 28% lower hydrogen concentrations, and 25% lower methane concentrations as compared to leak inlet locations near the centre of the cupboard.The work was supported by Cranfield University and DNV Energy Systems, UK

Vom Draht zum Bauteil: Entwicklung einer Aluminium-Lithium-Legierung für die additive Fertigung mit Draht und Lichtbogen

The Innovative Aluminium filler Wires for Aircraft Structures (IAWAS) project aimed to demonstrate the potential of Wire Arc Additive Manufacture (WAAM) for the production of aluminium lithium components. Preliminary testing demonstrated the possibility of depositing an 2395 aluminium lithium filler wire using a plasma arc heat source and a local shielding device. The deposit had a low porosity level but also low ductility caused by long, vertical, segregated grain boundaries. Both chemical composition and deposition conditions are known to impact the deposit microstructure. In-situ alloying, an efficient technique to develop new material, was implemented using plasma arc as a heat source on aluminium lithium alloys. The results aligned with the literature review on the impact of copper on crack sensitivity and led to the design of a new alloy. Unfortunately, the composition selected yielded challenges during the drawing process, and the filler material quality was poor, leading to a low WAAM deposit quality. Machine hammer peening was implemented on the AA2395 alloy, resulting in a drastic increase in ductility and yield strength of 480 MPa after solution treatment and ageing. This alloy was used to manufacture an aluminium lithium demonstrator to showcase the potential of WAAM to produce real-life components.The authors would like to acknowledge the European funding allocated to the IAWAS project partners (Grant agreement ID: 821371). This research work was also supported by the Engineering and Physical Sciences Research Council (EPSRC) through the NEwWire Additive