Digital twin-based approaches for agricultural machinery damage prediction and maintenance: A review

Abstract

The reliability of agricultural machinery is increasinglyer training speeds and higher recognition a constrained by harsh operating environments, complex dynamic loads, and evolving failure mechanisms, posing critical challenges to agricultural production efficiency and system resilience. Traditional maintenance methods, often reactive and resource-intensive, are insufficient to meet the demands of modern precision agriculture. There is a limited comprehensive review of how digital twin-based approaches can overcome these challenges by integrating data-driven models, intelligent prediction algorithms, and real-time maintenance decision-making strategies. Therefore, this paper reviews digital twin-based strategies for agricultural machinery damage prediction and maintenance optimization. Three key elements are analyzed: (1) numerical modeling approaches for simulating mechanical behavior and predicting damage evolution under diverse operational conditions; (2) advanced fault diagnosis techniques integrating machine learning algorithms and multi-source sensing to enable real-time monitoring, condition assessment, and early anomaly detection; (3) additive manufacturing (AM) technologies for the rapid repair and reinforcement of damaged components, supporting efficient lifecycle management. By integrating numerical simulation, intelligent diagnostics, and additive repair into digital twin frameworks, a predictive, closed-loop maintenance paradigm is established, enabling proactive interventions and enhanced operational continuity. Key challenges, including material and process limitations, portability and equipment adaptation, as well as model fidelity and real-time integration, are discussed. This review aims to provide a systematic reference for advancing digital twin technologies in agricultural machinery, which outlines future directions toward intelligent, sustainable, and resilient agricultural systems.