Defence Science and Technology Group (DSTG) is currently preparing for the launch of the Buccaneer Main Mission (BMM) satellite, the successor to the Buccaneer Risk Mitigation Mission (BRMM). BMM hosts a high-frequency (HF) antenna and receiver to contribute to the calibration of the Jindalee Operational Radar Network (JORN). Verification of the successful deployment and stability of the large HF antenna is critical to the success of the mission. A bespoke deployable optics payload has been developed by DSTG to fulfil the dual purpose of direct verification of the deployed state of the HF antenna and capturing images of the Earth through a rotatable, dual-surfaced mirror and a variable-focus liquid lens. The payload advances research at DSTG in several fields of space engineering, including deployable mechanisms, precision actuation devices, radiation-tolerant electronics, advanced metal polishing and optical metrology. This paper discusses the payload design, material selection, trade-offs considered for the deployable optics payload and preliminary test results.

Helicopter component load estimation can be achieved through a variety of machine learning techniques and algorithms. A range of ensemble integration techniques were investigated in order to leverage multiple machine learning models to estimate main rotor yoke loads from flight state and control system parameters. The techniques included simple averaging, weighted averaging and forward selection. Performance of the models was evaluated using four metrics: root mean squared error, correlation coefficient and the interquartile ranges of these two metrics. When compared, every ensemble outperformed the best individual model. The ensembles using forward selection achieved the best performance. The resulting output is more robust, more highly correlated and achieves lower error values as compared to the top individual models. While individual model outputs can vary significantly, confidence in their results can be greatly increased through the use of a diverse set of models and ensemble techniques.

In recent years, there has been significant momentum in applying deep learning (DL) to machine health monitoring (MHM). It has been widely claimed that DL methodologies are superior to more traditional techniques in this area. This paper aims to investigate this claim by analysing a real-world dataset of helicopter sensor faults provided by Airbus. Specifically, we will address the problem of machine sensor health unsupervised classification. In a 2019 worldwide competition hosted by Airbus, Fujitsu Systems Europe (FSE) won first prize by achieving an F1-score of 93% using a DL model based on generative adversarial networks (GAN). In another comprehensive study, various modified and existing image encoding methods were compared for the convolutional auto-encoder (CAE) model. The best classification result was achieved using the scalogram as the image encoding method, with an F1-score of 91%. In this paper, we use these two studies as benchmarks to compare with basic statistical analysis methods and the one-class supporting vector machine (SVM). Our comparative study demonstrates that while DL-based techniques have great potential, they are not always superior to traditional methods. We therefore recommend that all future published studies of applying DL methods to MHM include appropriately selected traditional reference methods, wherever possible.

Laser-directed energy deposition (L-DED) is a key enabling technology for the repair of high-value aerospace components, as damaged regions can be removed and replaced with additively deposited material. While L-DED repair improves strength and fatigue performance compared to conventional subtractive techniques, mechanical performance can be limited by process-related defects. To assess the role of oxygen on defect formation, local and chamber-based shielding methods were applied in the repair of 300M high strength steel. Oxidation between layers for locally shielded specimens is confirmed to cause large gas pores which have deleterious effects on fatigue life. Such pores are eliminated for chamber shielded specimens, resulting in an increased ductility of ∼15%, compared to ∼11% with chamber shielding. Despite this, unmelted powder defects are not affected by oxygen content and are found in both chamber- and locally shielded samples, which still have negative consequences for fatigue.

Even the shortest flight through unknown, cluttered environments requires reliable local path planning algorithms to avoid unforeseen obstacles. The algorithm must evaluate alternative flight paths and identify the best path if an obstacle blocks its way. Commonly, weighted sums are used here. This work shows that weighted Chebyshev distances and factorial achievement scalarising functions are suitable alternatives to weighted sums if combined with the 3DVFH* local path planning algorithm. Both methods considerably reduce the failure probability of simulated flights in various environments. The standard 3DVFH* uses a weighted sum and has a failure probability of 50% in the test environments. A factorial achievement scalarising function, which minimises the worst combination of two out of four objective functions, reaches a failure probability of 26%; A weighted Chebyshev distance, which optimises the worst objective, has a failure probability of 30%. These results show promise for further enhancements and to support broader applicability.

Prognostics and Health Management (PHM) models aim to estimate remaining useful life (RUL) of complex systems, enabling lower maintenance costs and increased availability. A substantial body of work considers the development and testing of new models using the NASA C-MAPSS dataset as a benchmark. In recent work, the use of ensemble methods has been prevalent. This paper proposes two adaptations to one of the best-performing ensemble methods, namely the Convolutional Neural Network – Long Short-Term Memory (CNN-LSTM) network developed by Li et al. (IEEE Access, 2019, 7, pp 75464–75475)). The first adaptation (adaptable time window, or ATW) increases accuracy of RUL estimates, with performance surpassing that of the state of the art, whereas the second (sub-network learning) does not improve performance. The results give greater insight into further development of innovative methods for prognostics, with future work focusing on translating the ATW approach to real-life industrial datasets and leveraging findings towards practical uptake for industrial applications.

A novel method of determining the crack tip location from a thermoelastic quadrature signal is presented. The method is utilised for crack tip locations within complex stress fields, namely within fastened aircraft lap joints. Coupled structural-thermal finite element modelling was undertaken to investigate the thermal response field around the crack tip location and develop the algorithmic principles. Experimental validation of the crack tip location was conducted using established crack mouth compliance techniques and optical measurements. The crack tip finding algorithm used the location of the maximum spatial gradient of the thermal field in the direction of crack growth. The method showed good accuracy when compared to traditional methods. Resultant crack growth rates were further verified using quantitative fractography.

High-fidelity simulation tools have significant potential to support composite aircraft sustainment, though further study is required on incorporating the complex impact damage field. In this paper, compressive residual strength assessment is investigated using the high-fidelity computational tool BSAMTM. The experimental impact damage was mapped and modelled at a high-fidelity level, which included ply-by-ply definition of the geometry of the impact indentation, fibre fracture in the plies and delamination in ply interfaces. It was shown that applying a small lateral displacement or ‘pseudo-impact’ step was highly effective in generating matrix cracks in the impact region, which provided a suitably realistic representation of the interconnected damage map through-the-thickness. It was found that inclusion of all damage modes in the post-image damage map at a high-fidelity definition was essential due to the strong degree of interaction between damage modes. The results support improved sustainment of defence platforms, through enhanced predictive capability and understanding.

Lamb waves are a growing method for the Non-Destructive Testing and Evaluation (NDT&E) and structural health monitoring (SHM) of aerospace vehicles. These guided waves can propagate over large distances and have a strong tendency to interact with damage. Whilst several methods exist for the modelling of Lamb wave propagation, this paper is the first to introduce a first principles numerical model that can efficiently and accurately predict the behaviour of Lamb waves. The numerical model is easier to understand and implement compared with analytical solutions and significantly faster than discretised numerical methods. The numerical model is presented in detail for an isotropic and homogenous plate, along with validation against the industry accepted, WaveForm Revealer 3 (WFR3) software. The results show a mean correlation across all assessed parameters of 90.4% and 96.6% for the symmetric and antisymmetric modes, respectively. Further discussion is provided on future developments to the model, including on the topic of high temperature effects, anisotropic materials and edge reflections.

A fleet of aircraft can be seen as a set of degrading systems that undergo variable loads as they fly missions and require maintenance throughout their lifetime. Optimal fleet management aims to maximise fleet availability while minimising overall maintenance costs. To achieve this goal, individual aircraft, with variable age and degradation paths, need to operate cooperatively to maintain high fleet availability while avoiding mechanical failure by scheduling preventive maintenance actions. In recent years, reinforcement learning (RL) has emerged as an effective method to optimise complex sequential decision-making problems. In this paper, an RL framework to optimise the operation and maintenance of a fleet of aircraft is developed. Three cases studies, with varying number of aircraft in the fleet, are used to demonstrate the ability of the RL policies to outperform traditional operation/maintenance strategies. As more aircraft are added to the fleet, the combinatorial explosion of the number of possible actions is identified as a main computational limitation. We conclude that the RL policy has potential to support fleet management operators and call for greater research on the application of multi-agent RL for fleet availability optimisation.