Multi-Domain Modeling and Simulation of an Aircraft System for Advanced Vehicle-Level Reasoning Research and Development

Abstract

Abstract-In this paper, we describe a simulation based health monitoring system test-bed for aircraft systems. The purpose of the test-bed is to provide a technology neutral basis for implementing and evaluation of reasoning systems on vehicle level and software architecture in support of the safety and maintenance process. This simulation test-bed will provide the sub-system level results and data which can be fed to the VLRS to generate vehicle level reasoning to achieve broader level diagnoses. This paper describes real-time system architecture and concept of operations for the aircraft major sub-systems. The four main components in the real-time test-bed are the aircraft sub-systems (e.g. battery, fuel, engine, generator, heating and lighting system) simulation model, fault insertion unit, health monitoring data processing and user interface. In this paper, we adopted a component based modelling paradigm for the implementation of the virtual aircraft systems. All of the fault injections are currently implemented via software. The fault insertion unit allows for the repeatable injection of faults into the system. The simulation test-bed has been tested with many different faults which were undetected on system level to process and detect on the vehicle level reasoning. This article also shows how one system fault can affect the overall health of the vehicle.

Platform health management for aircraft maintenance – a review

“…The fuel feed pipe was then damaged by this ice, which later burst, clogging the fuel oil heat exchanger and other fuel lines. Another example is the 2008 emergency evacuation of an Embraer 195 because of smoke in the cabin 78 where, after an inquiry into the incident, it was determined that both ACMs had suffered Stage 2 turbine blade failures. The resultant imbalance had resulted in contact between the turbine blade tips and the ACM casings, producing hot, finely divided, metallic particles that were released into the cabin air system, creating the reported symptoms of smoke and fumes inside the aircraft.…”

“…Diagnosing faults in scenarios like the abovementioned incidents, where failure in one component cascades into another component, and eventually into another system, requires a system that can detect faults as well as any interaction effects between components and systems. 79 This can be achieved by adopting a platform solution. Further, to support operational decisions, faults and interaction effect detection could be supplemented with information on the health index and criticality of the components.…”

“…Its application can be found in the use of digital twins to mimic the behaviour of systems or components to simulate what-if scenarios of actual systems. For examples, as discussed in Liu et al 85 and applied on an automotive brake pad for predictive maintenance in Rajesh et al 86 In an extended approach, there can be a hybrid of data-driven and model-based techniques. For instance, digital twins of systems can be developed, then used to produce data, which could be processed to derive insights into fault modes in the actual systems, as done with aircraft systems in Ezhilarasu et al 74 An expert system is derived from two fundamental concepts.…”

Connectors of smart design and smart systems

“…Bijlsma et al (2019) approached the issue of SLR from the perspective of the knowledge domain. Khan et al (2014) discussed a typical example of advanced vehicle-level reasoning in an aircraft system. As discussed by Reed and Pease (2017), algorithmic reasoning mechanisms confront obstacles when they operate on ambiguous, conditional, contradictory, fragmented, inert, uncertain, or imperfect knowledge.…”