English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The ability to predict the aircraft fuel system health/operating condition and possible complications that occur during the long flight of an aircraft helps to improve the performance of the aircraft engine. Prognostics and Health Management (PHM) methodology includes fault detection, diagnosis, and prognosis. In this paper, we propose an Artificial Neural Network (ANN) based fault prognosis tool for a typical aircraft fuel system. Prognostics method using ANN’s promise to provide a new approach to manage the fuel flow and fuel consumption of aircraft engine more effectively. This method identifies the presence of faults and mitigates them to maintain a proper fuel flow to the engine. Overlooking the presence of any faults in time could potentially be catastrophic which can lead to possible loss of lives and the aircraft as well. The developed tool works on the logical rules developed as per the engine’s fuel consumption and quantity of fuel flow from the tanks. Here, we discuss the algorithm and the results of using ANN models to predict the health condition of the fuel system of aircraft.

Simulation and Modeling of ANN-Based Prognosis Tool for a Typical Aircraft Fuel System Health Management