A Virtual Sensing approach for approximating nonlinear dynamical systems using LSTM networks

Publikation: Beitrag in FachzeitschriftKonferenzartikelBeigetragen

Abstract

In this contribution, we introduce a hybrid model for virtual sensing applications which combines a frequency response function model with a Long Short‐Term Memory network. It estimates the behavior of non‐linear dynamic systems with multiple input and output channels by generating predictions on short subsequences of signals and recombining them using a windowing technique. The approach is tested on an experimental dataset composed of measurements from a 3‐component servo hydraulic fatigue test bench. The model is parameterized using noise data, while fatigue serviceloads with variable amplitudes are used for validation and testing.

Details

OriginalspracheEnglisch
FachzeitschriftProceedings in Applied Mathematics and Mechanics: PAMM
Jahrgang21
Ausgabenummer1
PublikationsstatusVeröffentlicht - 14 Dez. 2021
Peer-Review-StatusNein

Externe IDs

ORCID /0000-0003-3358-1545/work/142237181
ORCID /0000-0002-7431-8973/work/142250145
Mendeley 68a51255-5742-3532-83e4-2e01794fe03d

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