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Abstract We introduce a novel digital twin framework for predictive maintenance of physical systems with long term operations. Using monitoring tire health as an application, we show how the digital twin framework is used to enhance automotive safety and efficiency, while overcoming technical challenges using a three-step approach. Firstly, for managing the data complexity over a long operation span, we employ data reduction techniques to concisely represent physical tires using historical performance and usage data. Relying on this data, for fast real-time prediction, we train a transformer-based model offline on our concise dataset to predict future tire health over time, represented as Remaining Casing Potential (RCP). Based on our architecture, our model quantifies both epistemic and aleatoric uncertainty, providing reliable confidence intervals around predicted RCP. Secondly, we incorporate real-time data by updating the predictive model in the digital twin framework, ensuring its accuracy throughout its life span with the aid of hybrid modeling and the use of a discrepancy function. Thirdly, to assist decision making in predictive maintenance, we implement a Tire State Decision Algorithm, which strategically determines the optimal timing for tire replacement based on RCP forecasted by our transformer model. This three-step approach ensures that our digital twin not only accurately predicts the health of a system, but also continually refines its digital representation and makes predictive maintenance decisions for removal from service. Our proposed digital twin framework embodies a physical system accurately and leverages big data and machine learning for predictive maintenance, model update and decision making.
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This content will become publicly available on August 20, 2026
Predictive Repair Management Using Multi-Head Attention Transformer and Online Learning
Accurate prediction of repair durations is a challenge in product maintenance due to its implications for resource allocation, customer satisfaction, and operational performance. This study aims to develop a deep learning framework to help fleet repair shops accurately categorize repair time given product historical data. The study uses an automobile repair and maintenance dataset and creates an end-to-end predictive framework by employing a multi-head attention network designed for tabular data. The developed framework combines categorical information, transformed through embeddings and attention mechanisms, with numerical historical data to facilitate integration and learning from diverse data features. A weighted loss function is introduced to overcome class imbalance issues in large datasets. Moreover, an online learning strategy is used for continuous incremental model updates to maintain predictive accuracy in evolving operational environments. Our empirical findings demonstrate that the multi-head attention mechanism extracts meaningful interactions between vehicle identifiers and repair types compared to a feed-forward neural network. Also, combining historical maintenance data with an online learning strategy facilitates real-time adjustments to changing patterns and increases the model’s predictive performance on new data. The model is tested on real-world repair data spanning 2013 to 2020 and achieves an accuracy of 78%, with attention weight analyses illustrating feature interactions.
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- PAR ID:
- 10636309
- Publisher / Repository:
- Proceedings of the ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE2025 August 17-20, 2025, Anaheim, California
- Date Published:
- Format(s):
- Medium: X
- Location:
- Anaheim, CA, USA
- Sponsoring Org:
- National Science Foundation
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