The aerospace industry is constantly looking to adopt new technologies to increase the performance of the machines and procedures they employ. In recent years, the industry has tried to introduce more electric aircraft and integrated vehicle health management technologies to achieve various benefits, such as weight reduction, lower fuel consumption, and a decrease in unexpected failures. In this experiment, data obtained from the simulation model of an electric braking system employing a brushed DC motor is used to determine its health. More specifically, the data are used to identify faults, namely open circuit fault, intermittent open circuit, and jamming. The variation of characteristic parameters during normal working conditions and when faults are encountered are analysed qualitatively. The analysis is used to select the features that are ideal to be fed into the reasoner. The selected features are braking force, wheel slip, motor temperature, and motor angular displacement, as these parameters have very distinct profiles upon injection of each of the faults. Due to the availability of clean data, a data-driven approach is adopted for the development of the reasoner. In this work, a Long Short-Term Memory Neural Network time series classifier is proposed for the identification of faults. The performance of this classifier is then compared with two others—K Nearest Neighbour time series and Time Series Forest classifiers. The comparison of the reasoners is then carried out in terms of accuracy, precision, recall and F1-score.

An efficient and all-inclusive health management encompassing condition-based maintenance (CBM) environment plays a pivotal role in enhancing the useful life of mission-critical systems. Leveraging high fidelity digital modelling and simulation, scalable to digital twin (DT) representation, quadruples their performance prediction and health management regime. The work presented in this paper does exactly the same for an electric braking system (EBS) of a more-electric aircraft (MEA) by developing a highly representative digital model of its electro-mechanical actuator (EMA) and integrating it with the digital model of anti-skid braking system (ABS). We have shown how, when supported with more-realistic simulation and the application of a qualitative validation approach, various fault modes (such as open circuit, circuit intermittence, and jamming) are implemented in an EMA digital model, followed by their impact assessment. Substantial performance degradation of an electric braking system is observed along with associated hazards as different fault mode scenarios are introduced into the model. With the subsequent qualitative validation of an EMA digital model, a complete performance as well as reliability profile of an EMA can be built to enable its wider deployment and safe integration with a larger number of aircraft systems to achieve environmentally friendly objectives of the aircraft industry. Most significantly, the qualitative validation provides an efficient method of identifying various fault modes in an EMA through rapid monitoring of associated sensor signals and their comparative analysis. It is envisaged that when applied as an add-on in digital twin environment, it would help enhance its CBM capability and improve the overall health management regime of electric braking systems in more-electric aircraft.

Fluid analog models for gravity are based on the idea that any spacetime geometry admits a reinterpretation in which space is thought of as a fluid flowing with a prescribed velocity. This fluid picture is a restatement of the ADM decomposition of the metric. Most of the literature has focused on flat spatial geometries and physical fluid flows, with a view toward possible laboratory realizations. Here we relax these conditions and consider fluid flows on curved and time-dependent spatial geometries, as a way of understanding and visualizing solutions to general relativity. We illustrate the utility of the approach with rotating black holes. For the Kerr black hole we develop a fluid description based on Doran coordinates. For spinning BTZ black holes we develop two different fluid descriptions. One involves static conical spatial slices, with the fluid orbiting the tip of the cone. The other resembles a cosmology, with the fluid flowing on a time-dependent cylindrical geometry.