【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。更多还原

【Abstract】 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.更多还原