Version 1
: Received: 13 June 2021 / Approved: 14 June 2021 / Online: 14 June 2021 (15:02:28 CEST)
How to cite:
Slyusar, V. The Concept of Networked Distributed Engine Control System of Future Air Vehicles. Preprints2021, 2021060372. https://doi.org/10.20944/preprints202106.0372.v1
Slyusar, V. The Concept of Networked Distributed Engine Control System of Future Air Vehicles. Preprints 2021, 2021060372. https://doi.org/10.20944/preprints202106.0372.v1
Slyusar, V. The Concept of Networked Distributed Engine Control System of Future Air Vehicles. Preprints2021, 2021060372. https://doi.org/10.20944/preprints202106.0372.v1
APA Style
Slyusar, V. (2021). The Concept of Networked Distributed Engine Control System of Future Air Vehicles. Preprints. https://doi.org/10.20944/preprints202106.0372.v1
Chicago/Turabian Style
Slyusar, V. 2021 "The Concept of Networked Distributed Engine Control System of Future Air Vehicles" Preprints. https://doi.org/10.20944/preprints202106.0372.v1
Abstract
This report is considered different aspects of the concept of the networked distributed engine control system (DECS) of future air vehicles. These aspects include the following: the structure of multiple networks similar to NATO Generic Vehicle Architecture (NGVA), the role of Artificial Intelligence (AI) in DECS, and the use Augmented Reality (AR) as Human-Machine Interface between AI and pilots. Deployment of AI solutions for monitoring equipment in on-board infrastructure can be provided on physical or virtual servers and in the clouds. In this case, it is possible to use various methods of alerting the pilot and ground personnel on the basis of AR. The use of AI allows covering an unlimited set of scenarios, to provide an assessment of the likelihood of equipment failure, classification alarm is normal, and recognition of the development of defects. To collect Big Data from sensors and the pre-processing of this data before a machine learning (ML) procedure it is proposed to form data sets with the help of the face-splitting matrix product. To decrease the time of reaction of Neural Networks it has been suggested the implementation of advanced tensor-matrix theory on the basis of penetrating face product of matrices. Other important results of the report are a possible version of the AR data format for DECS and a proposal about the use of non-orthogonal frequency discrete multiplexing (N-OFDM) signals to data transfer via fibre optics.
Keywords
distributed engine control system (DECS), NGVA, Data Distribution Service (DDS), Artificial Intelligence (AI), Augmented Reality (AR)
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
