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

Autor: Heindel, Leonhard, Hantschke, Peter, Kästner, Markus
Předmět:
Zdroj: PAMM: Proceedings in Applied Mathematics & Mechanics; Dec2021, Vol. 21 Issue 1, p1-2, 2p
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index