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Title: Progress towards data-driven high-rate structural state estimation on edge computing devices
Structures operating in high-rate dynamic environments, such as hypersonic vehicles, orbital space infrastructure, and blast mitigation systems, require microsecond (µs) decisionmaking. Advances in real-time sensing, edge-computing, and high-bandwidth computer memory are enabling emerging technologies such as High-rate structural health monitoring (HRSHM) to become more feasible. Due to the time restrictions such systems operate under, a target of 1 millisecond (ms) from event detection to decision-making is set at the goal to enable HRSHM. With minimizing latency in mind, a data-driven method that relies on time-series measurements processed in real-time to infer the state of the structure is investigated in this preliminary work. A methodology for deploying LSTM-based state estimators for structures using subsampled time-series vibration data is presented. The proposed estimator is deployed to an embedded real-time device and the achieved accuracy along with system timing are discussed. The proposed approach has shown potential for high-rate state estimation as it provides sufficient accuracy for the considered structure while a time-step of 2.5 ms is achieved. The Contributions of this work are twofold: 1) a framework for deploying LSTM models in real-time for high-rate state estimation, 2) an experimental validation of LSTMs running on a real-time computing system.  more » « less
Award ID(s):
1956071
NSF-PAR ID:
10435546
Author(s) / Creator(s):
Date Published:
Journal Name:
In Volume 10: 34th Conference on Mechanical Vibration and Sound (VIB). American Society of Mechanical Engineers, aug 2022.
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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