Digital Simulation and Identification of Faults with Neural Network Reasoners in Brushed Actuators Employed in an E-Brake System

Gouri Ramesh,Suresh Perinpanayagam,Pablo Garza

doi:10.3390/app11199171

Gouri Ramesh, Suresh Perinpanayagam + Show 1 more

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https://doi.org/10.3390/app11199171

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Journal: Applied Sciences	Publication Date: Oct 2, 2021
Citations: 4	License type: CC BY 4.0

Affiliation: Cranfield University

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.

Highlights

Ever since the inception of commercial aviation, civil aircraft have been relying on gas turbine engines to power up their systems
Two technologies that are rapidly and extensively being tested and deployed for better performance are more electric aircraft (MEA) and integrated vehicle health management (IVHM), as well as digital twins for different levels of fidelity in a variety of applications ranging from system development to vehicle health monitoring [1,2,3,4]
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

Summary

Introduction

Ever since the inception of commercial aviation, civil aircraft have been relying on gas turbine engines to power up their systems. As an industry that survives on wafer-thin profits and is bound by scores of safety regulations, all stakeholders, from manufacturers to operators, are continually looking to improve their methods to extract maximum efficiency from both the machines and humans they employ. In line with this goal, they are constantly testing new technologies to either replace existing systems or integrate with them. Owing to its paramount importance, maintenance work in the aerospace industry is undertaken at regular intervals according to pre-determined schedules This means that every aircraft, irrespective of its current health or its propensity to develop a fault, is grounded according to schedule. This, in turn, can cause a potential disruption in the maintenance schedule and overall airline operation, affecting the overall costs of the operation [5]

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