Vibration-Based Fault Diagnosis of Agricultural Machinery: A Review of Field Excitation, Signal Processing, Intelligent Models and Engineering Deployment

Agricultural machinery operates under complex field conditions involving uneven terrain, crop flow impacts, variable speed and load, dust, moisture, and multi-source structural excitation. These factors make vibration-based fault diagnosis more challenging than that of conventional rotating machinery because weak fault features are often masked by non-stationary background vibration and operating condition disturbances. This review provides a structured synthesis of vibration-based fault diagnosis for agricultural machinery, focusing on tractors, combine harvesters, harvesting machinery, and key components such as bearings, gearboxes, transmission systems, headers, threshing drums, cleaning sieves, vibrating screens, chassis, frames, and cab systems. The review first analyzes vibration sources, fault mechanisms, and signal degradation under field conditions. It then summarizes vibration sensors, data acquisition, preprocessing, time–frequency analysis, feature representation, machine learning, deep learning, transfer learning, and multi-source information fusion. Applications are reviewed from component-level diagnosis to whole-machine monitoring. Key challenges include field data scarcity, variable conditions, sensor reliability, data leakage, model generalization, edge deployment, standardization, and long-term validation. Future research should emphasise high-quality field datasets, physics-informed and explainable models, robust cross-condition diagnosis, multimodal sensing, edge intelligence, digital twins, and predictive maintenance. This review highlights the need to connect vibration mechanisms, diagnostic models, and engineering deployment requirements for reliable agricultural machinery health monitoring. Rather than treating sensors, components, algorithms, and deployment issues as separate topics, this review organizes the literature around field-specific vibration disturbances, validation evidence, deployable diagnostic requirements, and future implementation priorities.