摘要
针对发动机故障原因和征兆之间存在的复杂非线性关系,利用RIMER(基于证据推理算法的置信规则库推理方法)对发动机进行故障诊断,克服了传统专家系统或神经网络技术只能单一利用专家知识或训练数据的缺点,将定性知识与定量数据有效结合,对发动机故障原因进行了研究,给维修人员提供了重要参考依据,仿真实验结果表明该方法可行有效。
The beliefrule base inference methodology using the evidential reasoning approach(RIMER) expert system is applied to the fault diagnosis of engine to deal with the complex nonlinear relation between cause and symptom of fault,and it overcomes the disadvantage that the traditional expert system or neural network only uses expert knowledge or training data,investgates the fault cause and its possibility of engine,supplies the maintainer with important reference basis.The method is proved to be feasible and effective by the experiment results.
出处
《系统仿真技术》
2011年第1期11-15,共5页
System Simulation Technology
基金
国家自然科学基金重点课题资助项目(60736026)
关键词
专家系统
置信规则库
故障诊断
发动机
expert system
belief rule bases(BRB)
fault diagnosis
engine