Real-Time Machine Learning for High-Rate Structural Health Monitoring

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2022-LaflammeSimon-RealTime.pdf (805.16 KB)

Date

2021-10-24

Authors

Hu, Chao
Dodson, Jacob

Publisher

Springer, Cham

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Organizational Units

Civil, Construction and Environmental Engineering

Organizational Unit

Electrical and Computer Engineering

Organizational Unit

Center for Nondestructive Evaluation

Organizational Unit

Department

Civil, Construction and Environmental EngineeringElectrical and Computer EngineeringCenter for Nondestructive Evaluation

Abstract

Advances in science and engineering are empowering high-rate dynamic systems, such as hypersonic vehicles, advanced weaponries, and active shock and blast mitigation strategies. The real-time estimation of the structural health of high-rate systems, termed high-rate structural health monitoring (HRSHM), is critical in designing decision mechanisms that can ensure structural integrity and performance. However, this is a difficult task, because three aspects uniquely characterize these systems: (1) large uncertainties in the external loads; (2) high levels of non-stationarities and heavy disturbances; and (3) unmodeled dynamics generated from changes in system configurations. In addition, because these systems are experiencing events of high amplitudes (often beyond 100 g) over short durations (under 100 ms), a successful feedback mechanism is one that can operate under 1 ms. A solution to the unique system characteristics and temporal constraint is the design and application of real-time learning algorithms. Here, we review and discuss a real-time learning algorithm for HRSHM applications. In particular, after introducing the HRSHM challenge, we explore fast real-time learning for time series prediction using conventional and deep neural networks and discuss a path to rapid real-time state estimation.

Comments

This chapter is published as Laflamme S., Hu C., Dodson J. (2022) Real-Time Machine Learning for High-Rate Structural Health Monitoring. In: Cury A., Ribeiro D., Ubertini F., Todd M.D. (eds) Structural Health Monitoring Based on Data Science Techniques. Structural Integrity, vol 21. Springer, Cham. DOI: 10.1007/978-3-030-81716-9_4. Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.

Keywords

High-rate, Structural health monitoring, Neural network, Deep learning, Physics-informed machine learning