Design of Digital Twin Sensing Strategies Via Predictive Modeling and Interpretable Machine Learning

Kapteyn, Michael G.; Willcox, Karen E.

doi:10.1115/1.4054907

Design of Digital Twin Sensing Strategies Via Predictive Modeling and Interpretable Machine Learning

Journal Article · Fri Aug 05 00:00:00 EDT 2022 · Journal of Mechanical Design

DOI:https://doi.org/10.1115/1.4054907· OSTI ID:1903120

Kapteyn, Michael G. ^[1]; Willcox, Karen E. ^[2]

Univ. of Texas at Austin, TX (United States); University of Texas at Austin
Univ. of Texas at Austin, TX (United States)

This work develops a methodology for sensor placement and dynamic sensor scheduling decisions for digital twins. The digital twin data assimilation is posed as a classification problem, and predictive models are used to train optimal classification trees that represent the map from observed data to estimated digital twin states. In addition to providing a rapid digital twin updating capability, the resulting classification trees yield an interpretable mathematical representation that can be queried to inform sensor placement and sensor scheduling decisions. The proposed approach is demonstrated for a structural digital twin of a 12 ft wingspan unmanned aerial vehicle. Offline, training data are generated by simulating scenarios using predictive reduced-order models of the vehicle in a range of structural states. Furthermore, these training data can be further augmented using experimental or other historical data. In operation, the trained classifier is applied to observational data from the physical vehicle, enabling rapid adaptation of the digital twin in response to changes in structural health. Within this context, we study the performance of the optimal tree classifiers and demonstrate how they enable explainable structural assessments from sparse sensor measurements and also inform optimal sensor placement.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Texas at Austin, TX (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0021239

OSTI ID:: 1903120

Journal Information:: Journal of Mechanical Design, Journal Name: Journal of Mechanical Design Journal Issue: 9 Vol. 144; ISSN 1050-0472

Publisher:: ASMECopyright Statement

Country of Publication:: United States

Language:: English

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