Uncertainty Quantification and Runtime Monitoring Using Environment-Aware Digital Twins

Woodcock, Jim; Gomes, Cláudio; Macedo, Hugo Daniel; Larsen, Peter Gorm

doi:10.1007/978-3-030-83723-5_6

Cited by 14 publications

(6 citation statements)

References 29 publications

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“…As a result, UQ of both digital twin models and DNN models has already been studied in the literature. The authors in [13] made use of Monte Carlo co-simulations with a digital twin and adopted statistical analysis tools to quantify the uncertainty in controlling an agricultural vehicle. A Bayesian model was used for surrogate modelling and UQ of convolutional neural networks learning to solve stochastic partial differential equations in [14].…”

Section: Related Workmentioning

confidence: 99%

Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines

Das¹,

Gjorgiev²,

Sansavini³

2023

Preprint

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Deep neural networks (DNNs) are often coupled with physics-based models or data-driven surrogate models to perform fault detection and health monitoring of systems in the low data regime. These models serve as digital twins to generate large quantities of data to train DNNs which would otherwise be difficult to obtain from the real-life system. However, such models can exhibit parametric uncertainty that propagates to the generated data. In addition, DNNs exhibit uncertainty in the parameters learnt during training. In such a scenario, the performance of the DNN model will be influenced by the uncertainty in the physics-based model as well as the parameters of the DNN. In this article, we quantify the impact of both these sources of uncertainty on the performance of the DNN. We perform explicit propagation of uncertainty in input data through all layers of the DNN, as well as implicit prediction of output uncertainty to capture the former. Furthermore, we adopt Monte Carlo dropout to capture uncertainty in DNN parameters. We demonstrate the approach for fault detection of power lines with a physics-based model, two types of input data and three different neural network architectures. We compare the performance of such uncertainty-aware probabilistic models with their deterministic counterparts. The results show that the probabilistic models provide important information regarding the confidence of predictions, while also delivering an improvement in performance over deterministic models.

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Section: Related Workmentioning

confidence: 99%

Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines

Das¹,

Gjorgiev²,

Sansavini³

2023

Preprint

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“…subject purpose trucks [98] predict failures, plan maintenance electric vehicle, charging piles [83] evaluate efficiency of charging policies softwaredefined vehicular networks [105] verify routing schemes automated guided vehicle, factory floor [94] reduce collisions between multiple vehicles solar car [96] estimate energy consumption automated guided vehicle [95] ease development of robots, raise alerts when robot and simulation behaviour diverge autonomous haulers [97] identify potential platooning threats hybrid electric vehicle [100] notify drivers about failures vehicles, road [71] detect diversions, detect red light running, detect illegal parking vehicular edge networks [106] detect suspicious objects and behaviour vehicles [101] detect GNSS attacks agricultural vehicle [107] correct steering direction electric vehicle [103] predict energy consumption vehicles [104] detect unsafe traffic events vehicles, traffic [82] propose eco-routing strategies, mitigate carbon emissions [91].…”

Section: Table IV Applications Of Vehicle Digital Twins (Sans Trains)mentioning

confidence: 99%

A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation

Prikler,

Wotawa

2023

2023 10th International Conference on Dependable Systems and Their Applications (DSA)

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In recent years, digital twins have been proposed and implemented in various fields with potential applications ranging from prototyping to maintenance. Going forward, they are to enable numerous efficient and sustainable technologies, among them autonomous cars. However, despite a large body of research in many fields, academics have yet to agree on what exactly a digital twin is -and as a result, what its capabilities and limitations might be. To further our understanding, we explore the capabilities of digital twins concerning diagnosis in the field of transportation. We conduct a systematic mapping study including digital twins of vehicles and their components, as well as transportation infrastructure. We discovered that few papers on digital twins describe any diagnostic process. Furthermore, most existing approaches appear limited to system monitoring or fault detection. These findings suggest that we need more research for diagnostic reasoning utilizing digital twins.

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“…Another aspect of effective decision-making is uncertainty quantification [64] (capability 8), which has been achieved through Monte Carlo-based sampling [63] or the more efficient surrogate-based approaches like Gaussian processes [64]. Updating the digital system and uncertainty quantification with forward prediction can be a computational and time-intensive challenge that limits practical implementation.…”

Section: Uncertainty Quantification and Propagationmentioning

confidence: 99%

Digital Twins for the Designs of Systems: a Perspective

Beek¹,

Karkaria²,

Chen³

2022

Preprint

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The design and operation of systems are conventionally viewed as a sequential decision-making process that is informed by data from physical experiments and simulations. However, the integration of these highdimensional and heterogeneous data sources requires the consideration of the impact of a decision on a system's remaining life cycle. Consequently, this introduces a degree of complexity that in most cases can only be solved through an integrated decision-making approach. In this perspective paper, we use the digital twin concept to formulate an integrated perspective for the design of systems. Specifically, we show how the digital twin concept enables the integration of system design decisions and operational decisions during each stage of a system's life cycle. This perspective has two advantages: (i) improved system performance as more effective decisions can be made, and (ii) improved data efficiency as it provides a framework to utilize data from multiple sources and design instances. From the formal definition, we identify a set of eight capabilities that are vital constructs to bring about the potential, as defined in this paper, that the digital twin concept holds for the design of systems. Subsequently, by comparing these capabilities with the available literature on digital twins, we identify a set of research questions and forecast what their broader impact might be. By conceptualizing the potential that the digital twin concept holds for the design of systems, we hope to contribute to the convergence of definitions, problem formulations, research gaps, and value propositions in this burgeoning field. Addressing the research questions, associated with the digital twin-inspired formulation for the design of systems, will bring about more advanced systems that can meet some of the societies' grand challenges.

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Uncertainty Quantification and Runtime Monitoring Using Environment-Aware Digital Twins

Abstract: This is the accepted manuscript (post-print version) of the article.Contentwise, the accepted manuscript version is identical to the final published version, but there may be differences in typography and layout.

Cited by 14 publications

References 29 publications

Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines

Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines

A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation

Digital Twins for the Designs of Systems: a Perspective

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