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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.
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Reconceptualizing the Prognostics Digital Twin for Smart Manufacturing with Data-Driven Evolutionary Models and Adaptive Uncertainty Quantification
This work presents an integrated architecture for a prognostic digital twin for smart manufacturing subsystems. The specific case of cutting tool wear (flank wear) in a CNC machine is considered, using benchmark data sets provided by the Prognostics and Health Management (PHM) Society. This paper emphasizes the role of robust uncertainty quantification, especially in the presence of data-driven black- and gray-box dynamic models. A surrogate dynamic model is constructed to track the evolution of flank wear using a reduced set of features extracted from multi-modal sensor time series data. The digital twin's uncertainty quantification engine integrates with this dynamic model along with a machine emulator that is tasked with generating future operating scenarios for the machine. The surrogate dynamic model and emulator are combined in a closed-loop architecture with an adaptive Monte Carlo uncertainty forecasting framework that allows prediction of quantities of interest critical to prognostics within user-prescribed bounds. Numerical results using the PHM dataset are shown illustrating how the adaptive uncertainty forecasting tools deliver a trustworthy forecast by maintaining predictive error within the prescribed tolerance.
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- Award ID(s):
- 2317579
- PAR ID:
- 10557953
- Publisher / Repository:
- Prognostics and Health Management Society
- Date Published:
- Journal Name:
- Annual Conference of the PHM Society
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 2325-0178
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.36001/phmconf.2023.v15i1.3484
