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arXiv · 2107.03645

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

Leonhard Heindel·Peter Hantschke·Markus Kästner
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Abstract

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.

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Leonhard Heindel, Peter Hantschke, Markus Kästner. 2021-07-08. Assessment of hybrid machine learning models for non-linear system identification of fatigue test rigs. https://doi.org/10.1016/j.fraope.2024.100157

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