An official website of the United States government
Abstract Predictive Maintenance (PdM) emerges as a critical task of Industry 4.0, driving operational efficiency, minimizing downtime, and reducing maintenance costs. However, real-world industrial environments present unsolved challenges, especially in predicting simultaneous and correlated faults under evolving conditions. Traditional batch-based and deep learning approaches for simultaneous fault prediction often fall short due to their assumptions of static data distributions and high computational demands, making them unsuitable for dynamic, resource-constrained systems. In response, we propose OEMLHAT (Online Ensemble of Multi-Label Hoeffding Adaptive Trees), a novel model tailored for real-time, multi-label fault prediction in non-stationary industrial settings. OEMLHAT introduces a scalable online ensemble architecture that integrates online bagging, dynamic feature subspacing, and adaptive output weighting. This design allows it to efficiently handle concept drift, high-dimensional input spaces, and label sparsity, key bottlenecks in existing PdM solutions. Experimental results on three public multi-label PdM case studies demonstrate substantial improvements in predictive performance of OEMLHAT over previous batch-based and online proposals for multi-label classification, particularly with an average improvement in micro-averaged F1-score of 18.49% over the second most-accurate batch-based proposal and of 8.56% in the case of the second best online model. By addressing a critical gap in online multi-label learning for PdM, this work provides a robust and interpretable solution for next-generation industrial monitoring systems for fault detection, particularly for rare and concurrent failures.
more »
« less
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.
more »
« less
- 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
More Like this
-
-
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.more » « less
-
Abstract Predictive maintenance in truck fleet management is essential to reduce downtime and maintenance costs, yet traditional approaches often rely on static, rule-based schedules that fail to capture real-time operational variability. In this paper, we propose a robust digital twin (DT) framework for predictive maintenance specifically designed for tire predictive maintenance that integrates real-time tire health data, dynamic decision-making, and adaptive model updates to optimize tire resource allocation and enhance system health. Our framework is unique in its ability to incorporate uncertainty-aware dynamic programming, drift detection, and adaptive surrogate model updates within the digital twin. Specifically, we develop an uncertainty-aware dynamic linear programming (U-DLP) approach to optimize tire placement and servicing schedules based on continuously updated tire health data through surrogate model. To ensure DT reliability, we employ the maximum concept discrepancy (MCD) method to detect drift by identifying discrepancies between predicted and actual tire performance, thereby flagging data for necessary tire health model updates. Subsequently, we introduce an uncertainty-aware low-rank adaptation (U-LORA) method to efficiently update the tire health model by dynamically refining the surrogate model based on measured uncertainty. Simulation results indicate that our framework extends tire lifespan by nearly 50% compared to conventional methods, requiring fewer tires to achieve the same operational mileage, while also reducing tire waste and maintenance costs. This integrated digital twin framework offers a reliable and efficient solution for tire predictive maintenance, enhancing fleet sustainability and operational efficiency.more » « less
-
Abstract Accurate prediction of product failures and the need for repair services become critical for various reasons, including understanding the warranty performance of manufacturers, defining cost-efficient repair strategies, and compliance with safety standards. The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large data set of over 530,000 repairs and maintenance of medical devices has been investigated by employing the Support Vector Machine (SVM) tool. SVM with four kernel functions is used to forecast the timing of the next failure or repair request in the system for two different products and two different failure types, namely, random failure and physical damage. Frequency analysis is also conducted to explore the product quality level based on product failure and the time to repair it. Besides, the best probability distributions are fitted for the failure count, the time between failures, and the time to repair. The results reveal the value of data analytics and machine learning tools in analyzing post-market product performance and the cost of repair and maintenance operations.more » « less
-
Accurate prediction of product failures and the need for repair services become critical for various reasons, including understanding the warranty performance of manufacturers, defining cost-efficient repair strategies, and compliance with safety standards. The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large dataset of over 530,000 repairs and maintenance of medical devices has been investigated by employing the Support Vector Machine (SVM) tool. SVM with four kernel functions is used to forecast the timing of the next failure or repair request in the system for two different products and two different failure types, namely random failure and physical damage. A frequency analysis is also conducted to explore the product quality level based on product failure and the time to repair it. Besides, the best probability distributions are fitted for the number of failures, the time between failures, and the time to repair. The results reveal the value of data analytics and machine learning tools in analyzing post-market product performance and the cost of repair and maintenance operations.more » « less
