Capturing Lifecycle System Degradation in Digital Twin Model Updating

2025-03-11

AI Summary

To address the challenge of continuously characterizing full-lifecycle system degradation in digital twins without prior knowledge or offline experimentation, this paper proposes a lifelong adaptive updating method. The approach models system degradation as a dynamic evolution process of digital twin configurations—its first such formulation. It introduces a coupled autoencoder–LSTM framework for unsupervised, online, and generalizable temporal modeling of latent degradation features, enabling robust identification and representation of degradation stages. This feature representation drives dynamic parameter reconstruction of the digital twin, ensuring accurate response prediction across arbitrary degradation stages. Evaluated on two engineering datasets, the method significantly improves long-term predictive accuracy and enables autonomous evolution and adaptive reconfiguration of digital twin models—without requiring offline calibration.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Digital twin (DT) has emerged as a powerful tool to facilitate monitoring, control, and other decision-making tasks in real-world engineering systems. Online update methods have been proposed to update DT models. Considering the degradation behavior in the system lifecycle, these methods fail to enable DT models to predict the system responses affected by the system degradation over time. To alleviate this problem, degradation models of measurable parameters have been integrated into DT construction. However, identifying the degradation parameters relies on prior knowledge of the system and expensive experiments. To mitigate those limitations, this paper proposes a lifelong update method for DT models to capture the effects of system degradation on system responses without any prior knowledge and expensive offline experiments on the system. The core idea in the work is to represent the system degradation during the lifecycle as the dynamic changes of DT configurations (i.e., model parameters with a fixed model structure) at all degradation stages. During the lifelong update process, an Autoencoder is adopted to reconstruct the model parameters of all hidden layers simultaneously, so that the latent features taking into account the dependencies among hidden layers are obtained for each degradation stage. The dynamic behavior of latent features among successive degradation stages is then captured by a long short-term memory model, which enables prediction of the latent feature at any unseen stage. Based on the predicted latent features, the model configuration at future degradation stage is reconstructed to determine the new DT model, which predicts the system responses affected by the degradation at the same stage. The test results on two engineering datasets demonstrate that the proposed update method could capture effects of system degradation on system responses during the lifecycle.

Problem

• Captures system degradation effects in digital twin models.
• Eliminates need for prior knowledge and expensive experiments.
• Predicts system responses affected by lifecycle degradation.

Innovation

• Lifelong update method for digital twin models
• Autoencoder reconstructs hidden layer parameters
• LSTM captures dynamic latent feature behavior