Statistical and context-based diagnostics for Digital Twin environments
1
Tenneco Inc. ul. Usługowa 20, 44-100 Gliwice, Poland
2
Department of Fundamentals of Machinery Design, Silesian University of Technology,
ul. Konarskiego 18A, 44-100 Gliwice, Poland
Submission date: 2026-04-07
Final revision date: 2026-06-15
Acceptance date: 2026-06-22
Online publication date: 2026-06-23
Publication date: 2026-06-23
Corresponding author
Anna Maria Timofiejczuk
Department of Fundamentals of Machinery Design, Silesian University of Technology, ul. Konarskiego 18A, 44-100 Gliwice, Poland
Department of Fundamentals of Machinery Design, Silesian University of Technology, ul. Konarskiego 18A, 44-100 Gliwice, Poland
KEYWORDS
TOPICS
ABSTRACT
The Digital Twin concept is increasingly applied in engineering practice to support simulation-based design and validation processes. However, ensuring consistency between simulations and experimental data remains a critical challenge. In engineering systems identical statistical patterns can originate from different causes, so it may lead to incorrect diagnostic conclusions. This paper proposes an approach that integrates statistical validation methods with context-based reasoning to improve the interpretation of data in Digital Twin environments. The proposed framework combines data preprocessing techniques with statistical analysis and context-based reasoning. The applicability of the approach is shown through a representative case study. The results confirm that the integration of statistical methods with context-based reasoning enhances the robustness of Digital Twin systems and supports more effective use of simulation in modern engineering environments.
FUNDING
This research received no external funding.
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| eISSN: | 2449-5220 |
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