Zhang Shaocen, Zang Chongquan, Yang Zhang, Tang Lingyu, Wang Kun, Wang Anzhe, Chen Wenming, Song Qi, Wei Xinhua
School of Agricultural Engineering, Jiangsu University, Zhenjiang, China.
Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang, China.
Front Plant Sci. 2025 Jun 3;16:1577175. doi: 10.3389/fpls.2025.1577175. eCollection 2025.
This study proposes an IPSO-SVM-based fault prediction and fuzzy speed control system for unmanned combine harvesters. The primary goal is to prevent clogging failures and ensure long-term stable operation of unmanned harvesting machines, maintaining efficiency while minimizing downtime. The system integrates multi-component slip rate data, collected from critical parts of the harvester, and uses the IPSO-SVM model for fault warning. The fuzzy control algorithm adjusts the operating speed based on the predicted fault status and feeding rate to mitigate clogging risks. Experimental results show that the system can accurately identify over 98.5% of fault states and reduce the occurrence of complete blockage by adjusting the harvester's speed within 0.5 to 2 seconds after minor clogging. This work demonstrates the feasibility of applying the system in field environments, providing a reliable solution for the intelligent and unmanned operation of combine harvesters in fields.
本研究提出了一种基于改进粒子群优化支持向量机（IPSO-SVM）的无人联合收割机故障预测与模糊速度控制系统。其主要目标是防止堵塞故障，确保无人收割机长期稳定运行，在尽量减少停机时间的同时保持效率。该系统整合了从收割机关键部件收集的多部件滑移率数据，并使用IPSO-SVM模型进行故障预警。模糊控制算法根据预测的故障状态和喂入速率调整运行速度，以降低堵塞风险。实验结果表明，该系统能够准确识别超过98.5%的故障状态，并在轻微堵塞后0.5至2秒内通过调整收割机速度减少完全堵塞的发生。这项工作证明了该系统在田间环境中应用的可行性，为联合收割机在田间的智能化和无人化作业提供了可靠的解决方案。
