Fault Diagnosis of Intelligent Agricultural Vehicles based on Fault Trees

To address the issues of complex fault localization and difficult diagnosis in intelligent agricultural vehicles, this paper conducts a systematic analysis based on the Fault Tree Analysis method, considering structural composition, operating characteristics, and fault modes, and proposes corresponding improvement measures. The intelligent agricultural vehicle mainly consists of four functional modules: monitoring, mechanical operation, weeding, and spraying. Focusing on efficiency and precision problems in agricultural processes such as weeding, fertilizing, and pest control, the potential faults were hierarchically analyzed using the FTA method. The analysis results show that mechanical wear, electrical short circuits, sensor failures, and control system faults are the main fault modes. The root causes primarily include harsh environmental conditions, improper operation, and insufficient maintenance. The research findings provide a reference for subsequent reliability analysis and optimal design of agricultural equipment and contribute to the further development of agricultural mechanization and intelligence.