Advances in digital twinning for bearing fault diagnosis: a literature review

Abstract Bearing reliability is fundamental to guaranteeing the secure and efficient function of equipment. As industrial settings become increasingly intricate, fault diagnosis methods for bearings must evolve to offer greater precision and adaptability. Currently, the bearing fault diagnosis domain faces several challenges, including a shortage of failure data, high levels of environmental noise, and models that offer limited interpretability. Recently, digital twin (DT)—an innovative approach that synergistically combines various advanced techniques—has increasingly attracted attention as a promising solution to these issues. This paper begins by examining the evolution and practical applications of bearing fault diagnosis technologies. It first outlines the fundamental concepts of DT technology and fault diagnosis, detailing their historical development and the critical techniques involved. Next, this paper provides a comprehensive review of mainstream approaches to bearing fault diagnosis from two key perspectives: signal processing techniques and data-driven methods, outlining their respective strengths and limitations. To tackle the inherent challenges of these methods, existing DT systems for bearing fault diagnosis are categorized from a modeling standpoint. In addition, a practical DT implementation framework suited to real-world industrial applications is proposed. The paper further analyzes critical research challenges and offers targeted strategies and future directions. Overall, this work aims to offer both theoretical insights and practical guidance for advancing the integration of DT technology into intelligent bearing fault diagnosis.