Functional Requirements Enabling Levels of Predictive Maintenance Automation and Autonomy

Abstract

Artificial Intelligence (AI) supporting Digital Twins (DTs) has undoubtedly changed the ways predictive maintenance (PMx) is carried out on assets by enabling processes to be increasingly automated. However, without a standard definition for such evolution, this transformation lacks a solid foundation upon which to base its development. Other fields, namely, autonomous vehicles (AVs), use standardized levels of automation to outline coherent, agreed-upon criteria for AI-driven developments supporting autonomy that minimize barriers to interdisciplinary collaboration. In this work, we draw inspiration from the autonomy levels present in AV industry and propose levels of PMx DT automation. These levels define a clear path forward for AI-driven PMx DT developments. Motivated by our understanding that standardized processes for deploying AI-driven DTs (not only for PMx) in practice must have stakeholder buy-in that requires scalability, transferability, and integration into existing processes, we explore the functional requirements that facilitate systematic approaches at each of the proposed automation and autonomy levels.