作者：陈进1, 龚丽霞1, 李耀明2
作者单位：1. 江苏大学机械工程学院, 江苏 镇江 212013;
2. 江苏大学现代农业装备与技术教育部重点实验室, 江苏 镇江 212013
关键词：联合收割机; 模糊算法; 神经网络; 故障诊断
摘要：
为解决切纵流联合收割机故障诊断过程中输入量的非线性问题,设计基于FNN算法的联合收割机故障诊断系统。传感器采集待测部件的转速值为系统输入值,对输入值进行模糊处理得到模糊输入值,将模糊输入输出作为神经网络的输入输出,在Matlab中对神经网络进行离线训练得到故障诊断规则表,实际使用中只需在PLC中查询规则表即可得到故障诊断结果。实验结果表明:基于FNN的故障诊断系统能很好地解决系统的非线性问题,可以实时反映联合收割机的故障情况,使其尽可能保持高效率的工作状态。
Research of fault diagnosis system on combine-harvester based on FNN algorithm
CHEN Jin1, GONG Li-xia1, LI Yao-ming2
1. School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China;
2. The Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang 212013, China
Abstract: From a fault diagnosis perspective, combine-harvester is complex non-linear systems which consist of several inputs. In order to detect the various unexpected faults correctly and keep an effective work state, a fault diagnosis system was developed based on FNN algorithm and applied to the combine-harvester fault diagnosis system. Firstly, speed values are regarded as system input acquired by the sensor, and fuzzy inputs can be got by obfuscating the initial input. Secondly, the fuzzy inputs and outputs are treated as a neutral one, and it is easy to develop a troubleshooting rule table after Matlab offline training. Thirdly, the table look-up is the only thing we need to do to work out the fault diagnosis results in practical working. Further field experiments show that this system can solve the nonlinear problem and be a good reflection on the harvester fault conditions which directly lead to a high efficiency state.
Keywords: combine harvester; fuzzy algorithm; neural network; fault diagnosis
2014, 40(5): 77-79,83 收稿日期: 2013-10-23;收到修改稿日期: 2014-1-15
基金项目: 863计划项目(2012AA10A502);国家科技支撑项目(2010BAD01B06);江苏省科技支撑计划(BE2012312);镇江市科技支撑计划(NY2012001);无锡市科技成果产业化资金项目(CYE22C1216)
作者简介: 陈进(1959-),女,江苏盐城市人,教授,博士生导师,主要从事现代农业装备监测与控制技术研究。
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