Digital Twin-based IVHM for Predictive Maintenance
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Abstract
This paper proposes a Digital Twin-based Integrated Vehicle Health Management (IVHM) approach to enable predictive maintenance in aviation and aerospace industry. Predictive maintenance enables the identification of potential failure before they occur, improving operational efficiency, safety, and cost management by reducing downtown and optimizing maintenance scheduling. However, conventional approaches face three key challenges: lack of reliable run-to-failure data, uncertainties in system behavior and predictions, and fragmented processes between design and maintenance activities. This article introduces the concept of Authoritative Hybrid as-operated Digital Twin to overcome the current limitations. The proposed solution brings three main technical advancements: the integration of physics-informed Artificial Intelligence (AI) architecture reusing design artifacts into an IVHM system; the implementation of a comprehensive Validation, Verification, and Accreditation (VVA) process to support certification; and the enhancement of Model-Based Systems Engineering (MBSE) methods to ensure digital continuity across the different processes. This supports the development of advanced predictive maintenance capabilities, aligned with the vision of Type III IVHM systems, ultimately enabling more resilient, informed, and cost-effective operations in aerospace domain.
How to Cite
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Digital Twin, Predictive Maintenance, IVHM, Verification & Validation
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