Digital Twin-driven Vibration Monitoring and Fault Early Warning of Construction Cranes——A Review and Framework Proposal

Wang Chao

CCCC First Highway Engineering Co., Ltd., Fifth Engineering Co., Ltd

Abstract：

Construction cranes are indispensable heavy equipment in modern building projects, yet their structural failures caused by undetected fatigue and overload damage pose significant safety risks. Digital twin technology, which establishes real-time mapping between physical assets and virtual representations, has emerged as a transformative paradigm for structural health monitoring and predictive maintenance of crane systems. This paper presents a comprehensive review of digital twin-driven vibration monitoring and fault early warning systems specifically designed for construction cranes. We first introduce the fundamental architecture of a crane digital twin system, encompassing physical layer sensing, data transmission, virtual modeling, and intelligent decision-making modules. Subsequently, we review recent advances in vibration signal acquisition, deep learning-based fault diagnosis algorithms, and multi-fidelity surrogate modeling techniques that enable lightweight yet accurate digital twin implementations. Key enabling technologies, including Internet of Things (IoT) sensor networks, physics-based finite element modeling, and hybrid intelligence frameworks integrating physics-informed neural networks, are systematically examined. Furthermore, we discuss the practical challenges in deploying such systems on construction sites, including data quality assurance, real-time computational demands, and cyber-physical security. The paper concludes with a roadmap for future research directions, emphasizing the convergence of digital twins with edge computing and 5G communication to achieve autonomous crane health management in Industry 4.0 environments.

Key Words：

digital twin; construction crane; vibration monitoring; fault diagnosis; structural health monitoring; deep learning; predictive maintenance