2 research outputs found
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
A framework for aerospace vehicle reasoning (FAVER)
Airliners spend over 9% of their total revenue in Maintenance, Repair, and Overhaul
(MRO) and working to bring down the cost and time involved. The prime focus is on
unexpected downtime and extended maintenance leading to delays in the flights, which
also reduces the trustworthiness of the airliners among the customers. One of the effective
solutions to address this issue is Condition based Maintenance (CBM), in which the
aircraft systems are monitored frequently, and maintenance plans are customized to suit
the health of these systems. Integrated Vehicle Health Management (IVHM) is a
capability enabling CBM by assessing the current condition of the aircraft at component/
Line Replaceable Unit/ system levels and providing diagnosis and remaining useful life
calculations required for CBM. However, there is a lack of focus on vehicle level health
monitoring in IVHM, which is vital to identify fault propagation between the systems,
owing to their part in the complicated troubleshooting process resulting in prolonged
maintenance. This research addresses this issue by proposing a Framework for Aerospace
Vehicle Reasoning, shortly called FAVER. FAVER is developed to enable isolation and
root cause identification of faults propagating between multiple systems at the aircraft
level. This is done by involving Digital Twins (DTs) of aircraft systems in order to
emulate interactions between these systems and Reasoning to assess health information
to isolate cascading faults. FAVER currently uses four aircraft systems: i) the Electrical
Power System, ii) the Fuel System, iii) the Engine, and iv) the Environmental Control
System, to demonstrate its ability to provide high level reasoning, which can be used for
troubleshooting in practice. FAVER is also demonstrated for its ability to expand, update,
and scale for accommodating new aircraft systems into the framework along with its
flexibility. FAVER’s reasoning ability is also evaluated by testing various use cases.Transport System