智能数据挖掘在播种机故障检测中的应用

Application of Intelligent Data Mining in the Fault Detection of the Seeder

为进一步提高播种机的故障自诊断功能与综合作业效率,结合智能数据挖掘技术对其故障监测系统进行应用研究。通过系统性了解播种机作业机理与监控系统实现目标,建立了播种机故障检测流程,将发动机、变速箱体、齿轮传动等部位故障转换成数据代码,根据智能数据挖掘技术的闭环控制机理,搭建故障监测理论模型,生成播种机故障监测系统功能架构,并进行故障监测系统应用试验。结果表明:保持15~25区间的数据挖掘核心算法有效规则数,可信度平均可达到0.84以上,较传统控制算法提升了3%,平均故障检测准确率为95.37%,较一般故障诊断应用技术提升了5.0%,故障平均检测耗时缩短了46.55%,提高了播种机故障监测效率,可为播种机及类似农机向智能数据化改进提供一定思路。

In order to further improve the fault self-diagnosis function and comprehensive operation efficiency of agricultural seeder, the application of intelligent data mining technology to its fault monitoring system was studied. By systematically understanding the operation mechanism and the goal of the monitoring system of the seeder, the fault detection process of the agricultural seeder was formed, then the fault of the seeder engine, gearbox, gear transmission and other parts was converted into data code, and the fault monitoring mechanism of the seeder was established according to the closed-loop control mechanism of the application and analysis of intelligent data mining technology. On the basis of the model, the functional framework of the seeder fault monitoring system was generated, and the application test of the system was carried out. The results showed that keeping the number of effective rules of the core data mining algorithm in the 15-25 interval, the average reliability could reach more than 0.84, which was 3% higher than the traditional control algorithm, the average fault detection accuracy was 95.37%, 5.0% higher than the general fault diagnosis application technology, the average fault detection time was shortened by 46.55%, and the visualization of fault diagnosis display interface of the seeder was achieved, and it improved the monitoring efficiency of the seeder. The test results were good, which would provide some ideas for the intelligent data improvement of the seeder and similar agricultural machinery, be worth popularizing.