# State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies

arXiv: https://arxiv.org/abs/2311.06993 · HTML: https://mlsanity.com/paper/2311.06993/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 220 references that are in the map.
- First posted on arXiv 13 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A Survey of Predictive Maintenance: Systems, Purposes and Approaches](https://mlsanity.com/paper/1912.07383/) (2019)
- [From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability](https://mlsanity.com/paper/2002.02210/) (2020)
- [Fleet Prognosis with Physics-informed Recurrent Neural Networks](https://mlsanity.com/paper/1901.05512/) (2019)
- [Interpretable Categorization of Heterogeneous Time Series Data](https://mlsanity.com/paper/1708.09121/) (2017)
- [Physics-informed machine learning for sensor fault detection with flight test data](https://mlsanity.com/paper/2006.13380/) (2020)
- [The Digital Twin Landscape at the Crossroads of Predictive Maintenance, Machine Learning and Physics Based Modeling](https://mlsanity.com/paper/2206.10462/) (2022)
- [Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems](https://mlsanity.com/paper/2212.09240/) (2022)
- [Whole-Building Fault Detection: A Scalable Approach Using Spectral Methods](https://mlsanity.com/paper/1703.07048/) (2017)
- [Efficient Probabilistic Model Checking of Smart Building Maintenance using Fault Maintenance Trees](https://mlsanity.com/paper/1801.04263/) (2018)
- [A Scalable Predictive Maintenance Model for Detecting Wind Turbine Component Failures Based on SCADA Data](https://mlsanity.com/paper/1910.09808/) (2019)

## Built on by (most cited first, 4 of 4)

- [Bridging the Reality Gap in Digital Twins with Context-Aware, Physics-Guided Deep Learning](https://mlsanity.com/paper/2505.11847/) (2025)
- [Digital Twin Technologies in Predictive Maintenance: Enabling Transferability via Sim-to-Real and Real-to-Sim Transfer](https://mlsanity.com/paper/2507.18449/) (2025)
- [A Systematic Review of Digital Twin-Driven Predictive Maintenance in Industrial Engineering: Taxonomy, Architectural Elements, and Future Research Directions](https://mlsanity.com/paper/2509.24443/) (2025)
- [Grounding Time-Series Foundation Models in Digital Twin Topology for Predictive Maintenance](https://mlsanity.com/paper/2609.40071/) (2026)

## Abstract (arXiv)

Recent digital advances have popularized predictive maintenance (PMx), offering enhanced efficiency, automation, accuracy, cost savings, and independence in maintenance processes. Yet, PMx continues to face numerous limitations such as poor explainability, sample inefficiency of data-driven methods, complexity of physics-based methods, and limited generalizability and scalability of knowledge-based methods. This paper proposes leveraging Digital Twins (DTs) to address these challenges and enable automated PMx adoption on a larger scale. While DTs have the potential to be transformative, they have not yet reached the maturity needed to bridge these gaps in a standardized manner. Without a standard definition guiding this evolution, the transformation lacks a solid foundation for development. This paper provides a requirement-based roadmap to support standardized PMx automation using DT technologies. Our systematic approach comprises two primary stages. First, we methodically identify the Informational Requirements (IRs) and Functional Requirements (FRs) for PMx, which serve as a foundation from which any unified framework must emerge. Our approach to defining and using IRs and FRs as the backbone of any PMx DT is supported by the proven success of these requirements as blueprints in other areas, such as product development in the software industry. Second, we conduct a thorough literature review across various fields to assess how these IRs and FRs are currently being applied within DTs, enabling us to identify specific areas where further research is needed to support the progress and maturation of requirement-based PMx DTs.