  In recent years, predictive maintenance (PMx) has gained prominence for its
potential to enhance efficiency, automation, accuracy, and cost-effectiveness
while reducing human involvement. Importantly, PMx has evolved in tandem with
digital advancements, such as Big Data and the Internet of Things (IOT). These
technological strides have enabled Artificial Intelligence (AI) to
revolutionize PMx processes, with increasing capacities for real-time
automation of monitoring, analysis, and prediction tasks. However, PMx still
faces challenges such as poor explainability and sample inefficiency in
data-driven methods and high complexity in physics-based models, hindering
broader adoption. This paper posits that Digital Twins (DTs) can be integrated
into PMx to overcome these challenges, paving the way for more automated PMx
applications across various stakeholders. Despite their potential, current DTs
have not fully matured to bridge existing gaps. Our paper provides a
comprehensive roadmap for DT evolution, addressing current limitations to
foster large-scale automated PMx progression. We structure our approach in
three stages: First, we reference prior work where we identified and defined
the Information Requirements (IRs) and Functional Requirements (FRs) for PMx,
forming the blueprint for a unified framework. Second, we conduct a literature
review to assess current DT applications integrating these IRs and FRs,
revealing standardized DT models and tools that support automated PMx. Lastly,
we highlight gaps in current DT implementations, particularly those IRs and FRs
not fully supported, and outline the necessary components for a comprehensive,
automated PMx system. Our paper concludes with research directions aimed at
seamlessly integrating DTs into the PMx paradigm to achieve this ambitious
vision.
