On the Development of an AI-Based Tool to Assess the Instantaneous Modal Properties of Nonlinear SDOF Systems

In this article, a data-driven algorithm is developed to assess the natural frequency and damping ratio of a nonlinear oscillating single-degree-of-freedom (SDOF) system. The algorithm is based on hybrid convolutional–long short-term memory neural networks (CNN-LSTM) that process a short moving window belonging to a free-decay response and provide estimates of both parameters over time. The novelty of the study resides in the fact that the neural network is trained exclusively using synthetic data issued from linear SDOF models. Since the recurrent neural network (RNN) requires relatively small amounts of data to operate effectively, the nonlinear system locally behaves as a quasi-linear model, allowing each data segment to be processed under this assumption. The proposed RecuID tool is experimentally validated on a laboratory-scale nonlinear SDOF system. To demonstrate its effectiveness, it is compared to conventional identification algorithms. The experimental study yields a maximum mean absolute error (MAE) of 0.244 Hz for the natural frequency and 0.015 for the damping ratio. RecuID proves to be a faster and more robust methodology, capable of handling time-varying damping ratios up to 0.2 and a wide range of natural frequencies defined relative to the sampling rate.