农机电气装备故障快速诊断系统设计——基于知识挖掘和经验数据库
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摘要
随着现代农机设备自动化程度的不断提高,农机的电气系统也变得日益复杂,其故障诊断系统已经不能完全依赖于传统的专家系统,智能化的诊断系统更能够保证农机电气系统的正常运行。为此,将基于知识挖掘的神经网络算法和经验数据库与农机故障诊断理论相结合,综合运用神经网络交流故障数据库和神经网络直流故障数据库,形成了一种实用的分层神经网络故障字典诊断模型。为了验证模型的可行性,以拖拉机电气故障诊断为模型,对诊断系统的准确性进行了验证。测试结果表明:采用基于知识挖掘和经验数据库的电气故障诊断系统对于拖拉机电气系统故障的诊断是可行的,且诊断结果精度较高,可以满足智能化诊断的设计需求。
With the continuous improvement of the automation of modern agricultural machinery and equipment,the electrical system of agricultural machinery has become increasingly complex,and its fault diagnosis system has not completely depended on the traditional expert system. The intelligent diagnosis system system can ensure the normal operation of the agricultural machinery electrical system. According to this,the neural network algorithm based on knowledge mining and the experience database and the theory of agricultural machinery fault diagnosis are combined,and the neural network is used to communicate the fault database and the neural network DC fault database,and a practical hierarchical neural network fault dictionary diagnosis model is formed. In order to verify the feasibility of the model,the accuracy of the diagnosis system is verified by the tractor electrical fault diagnosis model. The test results show that the electrical fault diagnosis system based on knowledge mining and experience database is feasible for the diagnosis of the fault of the tractor electrical system,and its diagnosis results are fine. It is also high enough to meet the design requirements of intelligent diagnosis.
With the continuous improvement of the automation of modern agricultural machinery and equipment,the electrical system of agricultural machinery has become increasingly complex,and its fault diagnosis system has not completely depended on the traditional expert system. The intelligent diagnosis system system can ensure the normal operation of the agricultural machinery electrical system. According to this,the neural network algorithm based on knowledge mining and the experience database and the theory of agricultural machinery fault diagnosis are combined,and the neural network is used to communicate the fault database and the neural network DC fault database,and a practical hierarchical neural network fault dictionary diagnosis model is formed. In order to verify the feasibility of the model,the accuracy of the diagnosis system is verified by the tractor electrical fault diagnosis model. The test results show that the electrical fault diagnosis system based on knowledge mining and experience database is feasible for the diagnosis of the fault of the tractor electrical system,and its diagnosis results are fine. It is also high enough to meet the design requirements of intelligent diagnosis.
引文
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[25]郭钢祥,潘宏侠,王强龙.基于PSD和ADS7825的长管弯曲度测量系统的设计[J].煤矿机械,2013,34(4):268-270.
[26]张永肃,高宝成.基于Linux系统的汽车噪音故障诊断系统[J].嵌入式技术,2011,34(12):80-83.
[27]蔡国华,李慧,李洪文,等.基于ATmega128单片机的开沟深度自控系统试验台的设计[J].农业工程学报,2011,27(10):11-16.
[2]赵艺,陆超,韩英铎,等.基于改进无模型自适应控制算法的发电机广域阻尼控制器设计[J].清华大学学报:自然科学版,2013,53(11):1645-1652.
[3]裴新才,许同乐.小波分析在轴承故障特征信号降噪中的应用[J].山东理工大学学报:自然科学版,2011,25(2):89-91.
[4]贾继德,陈剑,邱峰.一种适用于非平稳、非线性振动信号分析方法研究[J].农业工程学报,2005,21(10):9-12.
[5]沈路,周晓军,刘莉,等.形态小波降噪方法在齿轮故障特征提取中的应用[J].农业机械学报,2010,41(4):217-221.
[6]张登攀,袁银南,杜家益,等.车用发动机停缸模式下转速波动仿真[J].农业机械学报,2010,41(2):25-28.
[7]李逃昌,胡静涛,高雷.基于模糊自适应纯追踪模型的农业机械路径追踪方法[J].农业机械学报,2013,44(1):205-210.
[8]吴斌,王敏捷,康晶,等.滚动轴承故障振动信号特征与诊断方法[J].大连理工大学学报,2013,53(1):76-81.
[9]黎丹,陈特放.机车轴承故障诊断中振动信号采集的研究[J].应用与设计,2009(1):35-39.
[10]郭京蔷,吴勇.DSP和PC机的串行通信[J].计算机工程与设计,2014,25(3):475-477.
[11]赵金辉,杨学军,周军平,等.播种机开沟器及其性能测试装置的现状分析[J].农机化研究,2014,36(1):238-241.
[12]孙星,吴俭敏,颜华,等.田间作业机具综合性能遥控系统[J].农机化研究,2013,35(7):97-100.
[13]郭孔辉,杨一洋,许男,等.轮胎试验台六分力解算、标定与优化分析[J].农业机械学报,2014,45(5):8-15.
[14]姚宗路,高焕文,王晓燕,等.小麦免耕播种机开沟器对作物生长的试验研究[J].农业工程学报,2007,23(7):117-121.
[15]刘立晶,杨学军,李长荣,等.2BMG-24型小麦免耕播种机设计[J].农业机械学报,2009,40(10):39-43.
[16]姚宗路,高焕文,李洪文,等.不同结构免耕开沟器对土壤阻力的影响[J].农机化研究,2009,31(7):30-34.
[17]李学军.便携式位移加速度振动信号测试系统软硬件设计与实现[J].电子测量与仪器学报,2008,22(2):56-61.
[18]高潮,田博今,郭永彩.基于DSP和ARM的自适应振动信号采集系统[J].计算机系统应用,2010,19(12):255-259.
[19]朱攀,贺敬.机载数据采集器模拟信号采集模块故障分析[J].中国科技信息,2014,7(11):173-175.
[20]陈科,张建庭,郑红梅,等.塔机信号采集与存储系统的设计与实现[J].电子测量与仪器学报,2014,28(8):892-900.
[21]刘建敏,李晓磊,乔新勇,等.基于EMD和STFT柴油机缸盖振动信号时频分析[J].噪声与振动控制,2013,4(2):133-137.
[22]王旭慧,江琦.小波分析在汽轮机振动故障诊断中的应用研究[J].电力学报,2014,29(1):80-83.
[23]胡爱军.经验模态分解中的模态混叠问题[J].振动、测试与诊断,2011,3(14):429-434.
[24]余建清,藏观建,谢世坤,等.旋转机械故障诊断中的信号处理技术综述[J].机床与液压,2011,39(24):107-110.
[25]郭钢祥,潘宏侠,王强龙.基于PSD和ADS7825的长管弯曲度测量系统的设计[J].煤矿机械,2013,34(4):268-270.
[26]张永肃,高宝成.基于Linux系统的汽车噪音故障诊断系统[J].嵌入式技术,2011,34(12):80-83.
[27]蔡国华,李慧,李洪文,等.基于ATmega128单片机的开沟深度自控系统试验台的设计[J].农业工程学报,2011,27(10):11-16.