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.

翻译：针对给定疲劳试验台驱动信号预测系统响应是一项具有挑战性的任务，目前广泛采用线性频率响应函数模型进行处理。为描述非线性现象，本文提出了一种新型混合模型，该模型通过长短期记忆网络增强现有方法。进一步展示了该方法在虚拟传感领域的应用潜力。采用伺服液压试验台的非线性实验数据对模型进行验证，并将该数据集公开共享。评估过程中引入了时域、频域的多类指标，以及变幅疲劳强度指标。