Simulation: the Critical Technology in Digital Twin Development

Biller, Bahar; Jiang, Wendy Xi; Yi, Jinxin; Venditti, Paul; Biller, Stephan

doi:10.1109/wsc57314.2022.10015246

Cited by 7 publications

(2 citation statements)

References 38 publications

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“…The question of batch sizing and spacing, that is, selecting m and d, is a subject of ongoing research (Su et al 2023). (g) Virtually all discussion in this paper applies to estimators constructed in the context of digital twins (Biller et al 2022). (h) Parametric batching, analogous to parametric bootstrap (Cheng 2017), has not been sufficiently explored and should form a topic of future research.…”

Section: Ten Further Points Of Discussionmentioning

confidence: 99%

Simulation-Estimator Inference

Pasupathy¹

2023

Proceedings of SW21 the OR Society Simulation Workshop

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Simulation, and now digital twins, excel in estimating parameters associated with complex and timedependent stochastic processes, e.g., large airport, highway, and warehouse operations. Within such contexts, we consider statistical inference, whereby one seeks to quantify the error in the obtained simulation estimate. Historically, statistical inference in simulation has been considered challenging because parameters needing estimation are often complicated, and simulation output often autocorrelated and non-normal. However, we argue that the remarkably simple idea of batching can be used as an "omnibus" inference device in simulation settings. Batching for inference works in three simple steps: (i) divide simulation output data into overlapping batches; (ii) construct parameter estimates from each batch; and (iii) use the batch estimates, after accounting for dependence, to perform statistical inference. As we describe in this paper, the resulting procedures are usually trivial to implement in software, and are provably correct and efficient. Batching ideas originated in the 1950s, and have enjoyed steady development in the simulation community since the 1970s mostly within the problem of variance estimation. Our thesis is that batching ideas have much wider utility, and that batching should be considered alongside other resampling ideas such as the bootstrap or the jackknife in modern statistics.

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Section: Ten Further Points Of Discussionmentioning

confidence: 99%

Simulation-Estimator Inference

Pasupathy¹

2023

Proceedings of SW21 the OR Society Simulation Workshop

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“…In general, most of the applications of predictive maintenance based on digital twin technology are developed from a physics-based model representation type; a few works can still be found in the literature in which the digital twins were developed based on a hybrid-approach model representation type, integrating real data with the results obtained from numerical simulations [19]. Indeed, digital twins can be developed based on historical data measured by sensors, simulation results, or as an integration of both [23][24][25][26][27][28]. The available data can be analyzed by means of non-deep learning methods, such as regression models, or deep-learning methods, such as deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin Based on Historical Data and Simulation Results: Fault Detection and Estimation of the Remaining Useful Life of a Cyclone Bag Filter

2023

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This study deals with the development of a digital twin for monitoring the operating conditions of a cyclone bag filter installed on the suction system of a wheat mill. The model aims to be used for fault identification and real-time prediction of the remaining useful life (RUL). Computational fluid dynamics simulations were performed to characterize in detail the fluid-dynamic behavior of the airflow inside the system under different conditions of filter sleeve clogging. Furthermore, the simulation results were used to identify a location for the installation of a new velocity sensor that would allow, together with the pressure drop measured at the ends of the filter, monitoring of the systems’ conditions. A model able to assess the filter’s operating state, identify failure events or operating anomalies, and make a prediction of the RUL was then developed. A possible implementation of the developed model, based on the simulation results that aimed to optimize the management of the sleeve cleaning cycles was also proposed. The developed digital model was then tested on a working cycle lasting one year, in which a sleeve failure was simulated. It was shown how the simultaneous monitoring of the two identified quantities allows for the correct identification of the failure and the accurate prediction of the RUL.

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