Scalable state change detection for stimulus-aware digital twins

Abstract

One of the fundamental functionalities of a Digital Twin of any production system is its ability to serve as a ‘twin’, i.e. effective synchronization between the physical state and the digital state. This ability of being stimulus-aware through effective detection of state change forms the foundation to realistically model production behavior in real-time for improved production planning, simulation, execution, and management. Application-based top-down approaches to developing Digital Twins generally consider pre-calculated states or known data conditions, limiting their scope. In contrast, real-life production presents a myriad of possible states. Thus, a bottom-up scalable state change detection method and a systematic four-phase learning framework using minimal data are proposed for realizing stimulus-aware Digital Twins at the beginning of equipment operation. The proposed solution uses two-stage pattern recognition and change detection learning using a single state, followed by new state detection and adaptation using a novel implementation of a custom loss function to maximize the distribution discrepancy between the latent representation of the two states. Validation is performed over three experimental datasets, including a newly created gearbox dataset that varies in type of equipment, signal, operating parameters, etc. 80 experiments are conducted across different scenarios and repeated five times each, along with statistical testing to benchmark performance with the traditional approach. The proposed method detects a change in state with over 97.5% accuracy and an F1 score of more than 95.2%, irrespective of the dataset or the states used for modeling, narrowing the gap between localized success stories and the large-scale use of Digital Twins in production industries. © 2026 Elsevier B.V.