Identification of Nonlinearities in Mechanical Structure Joints: A Systematic Review

Abstract

The identification of nonlinearities in mechanical structure joints is essential for improving predictive maintenance, structural health monitoring, and design optimization of engineering systems. Mechanical joints frequently exhibit complex nonlinear behaviors, including friction, backlash, hysteresis, and contact-induced effects, which can significantly influence the dynamic performance and reliability of structures. Although numerous studies have investigated these nonlinear phenomena, a comprehensive synthesis of identification techniques specifically addressing joint nonlinearities remains limited. This systematic review aims to bridge this gap by critically examining contemporary methodologies used to detect, characterize, and quantify nonlinear behavior in mechanical joints. The review focuses on three primary objectives: (i) classifying existing identification approaches, (ii) evaluating their effectiveness, applicability, and limitations, and (iii) identifying current challenges and future research opportunities. A systematic literature search was conducted across major engineering databases, encompassing experimental, numerical, and hybrid techniques for nonlinearity identification. Key methods, including nonlinear system identification, parameter estimation, model updating, and data-driven approaches, were comparatively analyzed. The findings indicate that frequency-domain and time-domain methods remain the most widely adopted techniques, while recent advances increasingly integrate machine learning and artificial intelligence to improve identification accuracy and robustness. Furthermore, the review highlights the growing importance of multi-physics and hybrid modeling frameworks for capturing complex joint behaviors under varying operational conditions. By consolidating and critically evaluating existing knowledge, this study provides a structured reference for selecting suitable identification methods and offers insights to support the development of more reliable mechanical systems, thereby contributing to advancements in structural health monitoring, maintenance, and engineering design.