Data-Driven Prediction of Friction Hysteresis Loops Using Temporal Deep Learning Models

Abstract

Friction hysteresis loops are critical for characterizing the nonlinear dynamic behavior of jointed structures. However, their complex nonlinear characteristics make efficient prediction challenging. In this study, a public high-frequency experimental dataset covering diverse operating conditions is adopted to investigate data-driven friction hysteresis loop prediction. Temporal datasets are constructed based on historical displacement information, and temporal deep learning models are developed for one-step-ahead friction force prediction. The prediction performance of TCN and GRU was evaluated across nine operating conditions, demonstrating their capability to accurately model friction hysteresis responses. Both models achieved comparable performance, with average R2 values exceeding 0.997 across all test conditions. TCN delivers marginally better overall accuracy and physical consistency, while GRU yields more stable predictions. Furthermore, physics-oriented metrics, including energy dissipation error and stiffness preservation error, are introduced to evaluate the physical consistency of predicted hysteresis responses. The proposed framework provides an effective data-driven approach for friction hysteresis prediction and offers potential support for nonlinear modeling and digital twin applications.