###### Abstract  
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. 20 September 20212 October  
to schedule. Although discovering unexpected faults during regular maintenance will prevent dangerous situations from happening, this finding will most likely lead to an extensive delay in the maintenance schedule. This is because the resources to resolve the fault may not be immediately available. This, in turn, can cause a potential disruption in the maintenance schedule and overall airline operation, affecting the overall costs of the operation [5]. Monitoring an aerospace vehicle's health can help evaluate the condition of the systems, prevent unnecessary maintenance, and increase the vehicle's useful life. In case abnormal equipment behaviour is observed during the vehicle operation, proper arrangements can be made to address the issue and decrease the probability of a dangerous situation. By using the data generated by the health monitoring systems, original equipment manufacturers (OEMs) benefit by using the data generated by the aircraft to improve the existing systems for more optimal performance or develop new ones more suited for operation [6]. With the increasing availability of data, development of technology with the internet-of-things (IoT) components, and the realisation that such data can be used to make better decisions for system design and maintainability, the field of IVHM is attracting high-profile stakeholders who are keen to determine and tap its full potential. Prognostics and diagnosis of aircraft systems carried out by health monitoring assets provide such stakeholders with valuable data to detect abnormalities, optimise maintenance schedules, and for better estimations of the system remaining useful life (RUL) [6; 7; 8]. For fault identification, reasoners developed from neural network (NN) algorithms become an asset for health management applications. By adequately training such reasoners, data obtained from the sensor systems can be used accordingly to provide insight into the overall system performance and modify the maintenance regime. A reasoner with good accuracy helps achieve the dual objective of saving time as well as other resources, such as manpower since it could potentially eliminate manual inspections of the target system [1; 9; 10]. The objective of this paper is to develop and propose a reasoner with good accuracy to identify selected faults that can occur in an aircraft electric braking system (EBS) developed from three different machine learning (ML) algorithms.