A Novel High-Level Reasoning Architecture for Aircraft Prescriptive Maintenance
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https://orcid.org/0000-0002-5918-0371
Fakhre Ali
Ian Jennions
Steve King
Martin Skote
Lusitha Ramachandra
Abstract
State-of-the-art Integrated Vehicle Health Management (IVHM) systems and digital twins (DTs) integrate physics-based and data-driven methodologies for predictive maintenance. Such systems commonly incorporate multiple DT instances to estimate substantive outputs. Nonetheless, they exhibit key limitations: lack of multi-DT orchestration mechanisms, limited uncertainty quantification, and insufficient prescriptive decision-support capability. To this end, this paper introduces an innovative High-Level Reasoner (HLR) decision support architecture for aircraft systems. The proposed HLR architecture comprises a multi-layer data-transfer structure, with the principal HLR layer consisting of adaptable specialist modules that facilitate a robust decision support implementation for query-driven prescriptive maintenance. The developed architecture is illustrated on an aircraft landing gear system (ATA 32), orchestrating multiple federated subsystems; represented by the Brake Temperature DT and Tyre Pressure DT. The contribution is a modular architecture that provides a reusable framework for extension across aircraft systems and wider IVHM applications. It therefore serves as an enabling technology that advances beyond existing diagnostics and prognostics solutions for asset utilisation.
How to Cite
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High-Level Reasoner (HLR), Integrated Vehicle Health Management, aircraft prescriptive maintenance, aviation digital twin, multi-DT orchestration, query-driven reasoning, aircraft landing gear health management, predictive maintenance decision support
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