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Recent digital advances have popularized predictive maintenance (PMx),offering enhanced efficiency, automation, accuracy, cost savings, andindependence in maintenance. Yet, it continues to face numerous limitationssuch as poor explainability, sample inefficiency of data-driven methods,complexity of physics-based methods, and limited generalizability andscalability of knowledge-based methods. This paper proposes leveraging DigitalTwins (DTs) to address these challenges and enable automated PMx adoption atlarger scales. While we argue that DTs have this transformative potential, theyhave not yet reached the level of maturity needed to bridge these gaps in astandardized way. Without a standard definition for such evolution, thistransformation lacks a solid foundation upon which to base its development.This paper provides a requirement-based roadmap supporting standardized PMxautomation using DT technologies. A systematic approach comprising two primarystages is presented. First, we methodically identify the InformationalRequirements (IRs) and Functional Requirements (FRs) for PMx, which serve as afoundation from which any unified framework must emerge. Our approach todefining and using IRs and FRs to form the backbone of any PMx DT is supportedby the track record of IRs and FRs being successfully used as blueprints inother areas, such as for product development within the software industry.Second, we conduct a thorough literature review spanning fields to determinethe ways in which these IRs and FRs are currently being used within DTs,enabling us to point to the specific areas where further research is warrantedto support the progress and maturation of requirement-based PMx DTs.

State-of-the-Art Review and Synthesis: A Requirement-based Roadmap for  Standardized Predictive Maintenance Automation Using Digital Twin  Technologies