摘要
为进一步优化我国农用拖拉机的结构布局与工作效率,创新性地融入大数据分析与智能传感技术,针对其发动机常见故障进行智能识别与监测研究。通过明确轮式拖拉机发动机的结构组成与工作原理,将数据深度挖掘与常见故障数据库有效结合,建立发动机故障监测模型,进行软件功能设计与硬件配置优化后形成完整的故障监测系统,并展开发动机常见故障智能监测试验。试验结果表明:经智能优化后的拖拉机发动机故障监测系统运行稳定可靠,平均故障识别时间可缩短至3.25s,故障识别准确率达到94.29%,相对提升了9.79%;发动机工作效率可提高至92.30%,有效降低了拖拉机整机停机率,验证了该智能设计应用的合理可行性,可为类似农机装备改善优化提供较好的优化思路。
In order to further optimize the structure layout and work efficiency of agricultural tractors in China,the big data analysis and the intelligent sensing technology were innovatively integrated into the system to carry out intelligent identification and monitoring of common engine faults.By defining the structure and working principle of the wheeled tractor engine,combining deep data mining with common fault database effectively,the engine fault monitoring model was established,and a complete fault monitoring system was formed after software function design and hardware configuration optimization.The intelligent monitoring test for common engine faults was carried out and the results showed that the tractor engine fault monitoring system was stable and reliable after the intelligent optimization,the average fault identification time could be shortened to 3.25s,the fault identification accuracy rate can reach 94.29%,which is relatively increased by 9.79%;The working efficiency of the engine could be increased to 92.30%,and the stopping rate of the whole tractor was also be reduced effectively,which verified the reasonable feasibility of the intelligent design and application,and would provide some better optimization ideas for the improvement and optimization of similar agricultural machinery equipment.
作者
袁苗达
Yuan Miaoda(Chongqing Industry Polytechnic College,Chongqing 421120,China)
出处
《农机化研究》
北大核心
2022年第11期248-252,共5页
Journal of Agricultural Mechanization Research
基金
全国机械职业教育“十二五”规划专项(JXHZW20140106)。
关键词
轮式拖拉机
发动机
故障数据库
监测模型
故障识别准确率
wheeled tractor
engine
fault database
monitoring model
the fault identification accuracy rate
