Digital simulation and identification of faults with neural network reasoners in brushed actuators employed in an e-brake system

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.