摘要
为了解决汽车故障诊断中的不确定性和建模问题,提出了一种基于非线性模糊关联特征的汽车故障挖掘算法,通过对汽车突发的各种故障中的非线性信息进行整理,在贝叶斯网络的学习能力和概率推理的基础上,加入模糊关联的推理机制,使得其对大量的非线性传感数据的分布特征,实现故障诊断系统的自适应,完成汽车故障特征的有效挖掘。实验结果表明,该方法能为故障诊断提供准确和可靠的决策依据。
In order to solve the automobile fault diagnosis of the uncertainty and modeling problem,this paper proposes a fuzzy correlation based on nonlinear characteristics of automobile fault mining algorithm,based on a car all kinds of fault information of nonlinear arrangement,in bayesian network learning ability and probability reasoning,on the basis of fuzzy association to join the reasoning mechanism,making the a large number of nonlinear sensing data distribution features and to implement fault diagnosis system of adaptive,complete automobile fault characteristics of the effective mining.The experimental results show that this method can provide accurate and reliable fault diagnosis decision-making basis.
出处
《科技通报》
北大核心
2013年第4期97-99,共3页
Bulletin of Science and Technology
关键词
故障诊断
模糊挖掘
贝叶斯网络
fault diagnosis
fuzzy mining
bayesian network
