Abstract
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.
Highlights
Any aerospace vehicle, like an aircraft, is a complex machine comprising various multi-physical systems, each having functions and objectives of their own
This paper proposes a framework that aims to isolate the cascading faults affecting multiple systems of the aircraft
In order to diagnose faults at the vehicle level, FAVER requires the reasoning element to meet with the following objectives: i) to process data from multiple aircraft systems, ii) to assess information, iii) set priorities, iv) resolve conflicts, v) pass judgement on the possible root causes of any fault, and vi) to update FAVER’s knowledge of any new fault that affects a system
Summary
Like an aircraft, is a complex machine comprising various multi-physical systems, each having functions and objectives of their own. Due to the interactions between systems, it is not uncommon for a fault arising from one system to propagate and affect another system that the former is interacting with Such cascading faults, whose paths are already known, are isolated in maintenance and troubleshooting activities. Further root cause analysis found that the fuel remained in the ‘sticky’ temperature range (less than 10oC) for a prolonged period of time; this resulted in ice formation, which in turn was released in the fuel feed pipe and blocked the fuel oil heat exchanger and the rest of the fuel lines Another such example is the emergency evacuation of a Fokker F28 in 2002, due to smoke in the cabin (Conradi, 2015). The Framework for Aerospace VEhicle Reasoning, known as FAVER, incorporates the concept of a Digital Twin (DT) to produce simulations of what-if scenarios between the aircraft systems, along with an element of reasoning, to investigate data from the concerned systems and to isolate the root cause of cascaded faults
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