From Passive Redundancy to Active Perception: A Comprehensive Review of AI-Enhanced Fault Diagnosis and Digital Twin Simulation for Combine Harvesters

Combine harvesters operate under harsh, time-varying field conditions, where unplanned downtime causes significant timeliness losses. This review systematically examines advances in structural fault diagnosis for combine harvesters, tracing the evolution from passive structural redundancy to active state perception and simulation-driven health management. Following a systematic search and screening methodology, the review analyzes the boundaries of structural optimization under fluctuating field conditions, evaluates traditional machine learning (ML) methods with handcrafted features, and surveys deep learning (DL) and multi-sensor fusion advances across vibration, acoustic, and visual modalities. A structured comparison across feature learning, generalization, diagnostic coverage, computational cost, and interpretability highlights the complementary strengths of traditional ML and DL paradigms. Key deployment challenges are identified: weak fault features under strong field noise, data distribution shift under multi-condition coupling, extreme sample scarcity and class imbalance, limited onboard computing and real-time latency constraints, interpretability gaps, hydraulic and pneumatic diagnostic neglect, functional safety compliance (ISO 25119), and the heightened reliability demands of unmanned autonomous operation. To address these challenges, future directions include physics-informed hybrid models, self-supervised pre-training and few-shot learning, lightweight edge inference, staged pre-deployment verification, model updating and lifelong learning strategies, cross-energy-domain diagnosis with fault-tolerant control, human-factors-aware interface design, and digital twin (DT) augmentation—the latter regarded as a strategic research vision requiring incremental validation rather than a near-term deployable solution. This review provides a reference for enhancing combine harvester mission reliability through the integration of AI-enabled perception, multi-source fusion, and simulation-driven health management.