A Virtual Sensing approach for approximating nonlinear dynamical systems using LSTM networks
Publikation: Beitrag in Fachzeitschrift › Konferenzartikel › Beigetragen
Beitragende
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
Originalsprache | Englisch |
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Fachzeitschrift | Proceedings in Applied Mathematics and Mechanics: PAMM |
Jahrgang | 21 |
Ausgabenummer | 1 |
Publikationsstatus | Veröffentlicht - 14 Dez. 2021 |
Peer-Review-Status | Nein |
Externe IDs
ORCID | /0000-0003-3358-1545/work/142237181 |
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ORCID | /0000-0002-7431-8973/work/142250145 |
Mendeley | 68a51255-5742-3532-83e4-2e01794fe03d |