Assessment of hybrid machine learning models for non-linear system identification of fatigue test rigs

Autor:	Heindel, Leonhard, Hantschke, Peter, Kästner, Markus
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1016/j.fraope.2024.100157
Popis:	The prediction of system responses for a given fatigue test bench drive signal is a challenging task, for which linear frequency response function models are commonly used. To account for non-linear phenomena, a novel hybrid model is suggested, which augments existing approaches using Long Short-Term Memory networks. Additional virtual sensing applications of this method are demonstrated. The approach is tested using non-linear experimental data from a servo-hydraulic test rig and this dataset is made publicly available. A variety of metrics in time and frequency domains, as well as fatigue strength under variable amplitudes, are employed in the evaluation. Comment: 20 pages, 11 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2107.03645 Zobrazit plný text záznamu View this record from Arxiv