Proceeding of the Faculty of technical Sciences, Novi Sad

Information engineering

Vol. 41 No. 04 (2026): Proceedings of the Faculty of Technical Sciences

Digital twins in predictive maintenance using artificial intelligence

  • Сања Тица
DOI:
https://doi.org/10.24867/34OI02Tica
Submitted
March 30, 2026
Published
2026-04-21

Abstract

This paper represents a systematic literature review on the topic of digital twins in predictive maintenance using artificial intelligence. The paper begins with a presentation of the theoretical foundations, introducing the reader to the key concepts. Subsequently, the methodology for searching and selecting research papers is described step by step. The results of the analysis are also presented graphically, through various diagrams that provide a visual overview of the current state and trends in the field. In the final part of the paper, the results of the content analysis of the selected papers from the first phase are presented, emphasizing how these studies contribute to answering the defined research questions and to the development of the field of digital twins in the context of predictive maintenance.

References

  1. [1] Traar G. Henjes J. Kritzinger, M. Karner and W. Sihn. Digital twin in manufacturing: A categorical literature review and classification. IFA PapersOnLine, 51(11).
  2. [2] Achouch, M. Dimitrova, K. Ziane, S. Sattarpanah Karganroudi, R. Dhouib, H. Ibrahim, and M. Adda. On predictive maintenance in industry 4.0: Overview, models, and challenges. Applied Sciences, 12(16), 2022.
  3. [3] Logan Cummins et al. Explainable predictive maintenance: A survey of current methods, challenges and opportunities. IEEE Access, 12:57574–57602, 2024.
  4. [4] J. Page, J. E. McKenzie, P. M. Bossuyt, et al. The prisma 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, 2021.
  5. [5] J. Burnham. Scopus database: A review. Biomedical Digital Libraries, 3, 2006.
  6. [6] IEEE Xplore. About content in ieee xplore. https://ieeexplore.ieee.org/Xplorehelp/overview-of-ieee-xplore/about-content. [Online; accessed 16 October-2025].
  7. [7] S. Ma, K. A. Flanigan, and M. Bergés. State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using dig- ital twin technologies. Advanced Engineering Informatics, 62, 2024.