Reusing Model Validation Methods for the Continuous Validation of Digital Twins of Cyber-Physical Systems
- URL: http://arxiv.org/abs/2512.04117v1
- Date: Mon, 01 Dec 2025 17:22:15 GMT
- Title: Reusing Model Validation Methods for the Continuous Validation of Digital Twins of Cyber-Physical Systems
- Authors: Joost Mertens, Joachim Denil,
- Abstract summary: One of the challenges in twinned systems is ensuring the digital twin remains a valid representation of the system it twins.<n>We provide a generic approach that, through the use of validation metrics, is able to detect anomalies in twinned systems.<n>Treating anomalies also means correcting the error in the digital twin, which we do with a parameter estimation based on the historical data.
- Score: 0.12031796234206132
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: One of the challenges in twinned systems is ensuring the digital twin remains a valid representation of the system it twins. Depending on the type of twinning occurring, it is either trivial, such as in dashboarding/visualizations that mirror the system with real-time data, or challenging, in case the digital twin is a simulation model that reflects the behavior of a physical twinned system. The challenge in this latter case comes from the fact that in contrast to software systems, physical systems are not immutable once deployed, but instead they evolve through processes like maintenance, wear and tear or user error. It is therefore important to detect when changes occur in the physical system to evolve the twin alongside it. We employ and reuse validation techniques from model-based design for this goal. Model validation is one of the steps used to gain trust in the representativeness of a simulation model. In this work, we provide two contributions: (i) we provide a generic approach that, through the use of validation metrics, is able to detect anomalies in twinned systems, and (ii) we demonstrate these techniques with the help of an academic yet industrially relevant case study of a gantry crane such as found in ports. Treating anomalies also means correcting the error in the digital twin, which we do with a parameter estimation based on the historical data.
Related papers
- Data-Driven Supervision of a Thermal-Hydraulic Process Towards a Physics-Based Digital Twin [2.0135920996943604]
This paper develops a digital twin dedicated to fault detection and diagnosis in the context of a thermal-hydraulic process supervision.<n>The proposed fault detection and diagnosis algorithm is validated on a specific test scenario.
arXiv Detail & Related papers (2026-02-25T07:09:39Z) - Cyber-Resilient System Identification for Power Grid through Bayesian Integration [49.3054872760439]
Power grids increasingly need real-time situational awareness under the ever-evolving cyberthreat landscape.<n>This work advances system identification that combines snapshot-based method with time-series model via Bayesian Integration.
arXiv Detail & Related papers (2025-10-15T19:32:09Z) - Continuously Updating Digital Twins using Large Language Models [49.7719149179179]
Digital twins are models of real-world systems that can simulate their dynamics in response to potential actions.<n>Current approaches struggle in this regard, as they require fixed, well-defined modelling environments.<n>We develop CALM-DT, a Context-Adaptive Language Model-based Digital Twin that can accurately simulate across diverse state-action spaces.
arXiv Detail & Related papers (2025-06-11T14:45:28Z) - Contract-based Verification of Digital Twins [1.6238978627325962]
This paper introduces an innovative methodology for verifying neural network-based digital twin models.<n>The latter relies on defining and applying system-level contracts that capture the system's requirements.<n>We develop an automated solution that simulates the digital twin model for certain inputs, and feeds the predicted outputs together with the inputs to the contract model.
arXiv Detail & Related papers (2025-04-07T10:33:10Z) - Formal Verification of Digital Twins with TLA and Information Leakage Control [15.387256204743407]
Digital twin verification is challenging due to uncertainties in the virtual representation, the physical environment, and the bidirectional flow of information between physical and virtual.<n>This paper presents a methodology to specify and verify digital twin behavior, translating uncertain processes into a formally verifiable finite state machine.<n>We demonstrate this approach on a digital twin of an unmanned aerial vehicle, verifying synchronization of physical-to-virtual and virtual-to-digital data flows to detect unintended misalignments.
arXiv Detail & Related papers (2024-11-27T22:52:36Z) - Representing Timed Automata and Timing Anomalies of Cyber-Physical
Production Systems in Knowledge Graphs [51.98400002538092]
This paper aims to improve model-based anomaly detection in CPPS by combining the learned timed automaton with a formal knowledge graph about the system.
Both the model and the detected anomalies are described in the knowledge graph in order to allow operators an easier interpretation of the model and the detected anomalies.
arXiv Detail & Related papers (2023-08-25T15:25:57Z) - Enhanced multi-fidelity modelling for digital twin and uncertainty
quantification [0.0]
Data-driven models play a crucial role in digital twins, enabling real-time updates and predictions.
The fidelity of available data and the scarcity of accurate sensor data often hinder the efficient learning of surrogate models.
We propose a novel framework that begins by developing a robust multi-fidelity surrogate model.
arXiv Detail & Related papers (2023-06-26T05:58:17Z) - Interactive System-wise Anomaly Detection [66.3766756452743]
Anomaly detection plays a fundamental role in various applications.
It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data.
We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
arXiv Detail & Related papers (2023-04-21T02:20:24Z) - Probabilistic machine learning based predictive and interpretable
digital twin for dynamical systems [0.0]
Two approaches for updating the digital twin are proposed.
In both cases, the resulting expressions of updated digital twins are identical.
The proposed approaches provide an exact and explainable description of the perturbations in digital twin models.
arXiv Detail & Related papers (2022-12-19T04:25:59Z) - Automatic digital twin data model generation of building energy systems
from piping and instrumentation diagrams [58.720142291102135]
We present an approach to recognize symbols and connections of P&ID from buildings in a completely automated way.
We apply algorithms for symbol recognition, line recognition and derivation of connections to the data sets.
The approach can be used in further processes like control generation, (distributed) model predictive control or fault detection.
arXiv Detail & Related papers (2021-08-31T15:09:39Z) - A Probabilistic Graphical Model Foundation for Enabling Predictive
Digital Twins at Scale [0.0]
We create an abstraction of the asset-twin system as a set of coupled dynamical systems.
We demonstrate how the model is instantiated to enable a structural digital twin of an unmanned aerial vehicle.
arXiv Detail & Related papers (2020-12-10T17:33:59Z) - A Novel Anomaly Detection Algorithm for Hybrid Production Systems based
on Deep Learning and Timed Automata [73.38551379469533]
DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems.
It combines deep learning and timed automata for creating behavioral model from observations.
The algorithm has been applied to few data sets including two from real systems and has shown promising results.
arXiv Detail & Related papers (2020-10-29T08:27:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.