This paper proposes a reasoning framework to diagnose faults at the vehicle level in a complex machine like an aircraft. The current focus of Integrated Vehicle Health Management (IVHM) is on diagnosing and prognosing faults at the component and subsystem levels; only a few IVHM systems consider the interaction between the systems. To diagnose faults at the vehicle level, an IVHM System needs a framework that recognizes the causal relationships between systems and the likelihood of fault propagation between them. The framework should also possess an element of reasoning to assess data from all systems, to assign priorities, and to resolve ambiguities. The Framework for Aerospace VEhicle Reasoning (FAVER) that is proposed in this paper uses a digital twin of the aircraft systems to emulate functioning of the aircraft and to simulate the effect of fault propagation due to systems interactions. FAVER applies reasoning that can handle fault signatures from multiple systems in the form of symptom vectors, to detect and isolate cascading faults and their root causes. The blending of a digital twin and reasoning in this framework will enable FAVER to: i) isolate faults that have both local and cascading effects on the concerned systems, ii) identify faults that were previously unknown, and iii) resolve ambiguous faults. This paper explains the different steps involved in developing FAVER and how this framework can be demonstrated in the aforementioned scenarios with the help of different use cases. This paper also talks about the challenges to be faced while developing this framework and ways to overcome them.
How to Cite
IVHM, Reasoning, Digital twin, Framework, Fault propagation
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.