摘要
介绍了RH-KTB炉外精炼真空抽气系统的工作原理和故障特点。该系统的故障诊断应能从大量数据中快速提取征兆并及时诊断,且有一定的自学习能力。为此,采用一种以实例为基础的归纳学习算法即ID3算法进行故障诊断。ID3算法有一定的自学习、自组织能力,适用于复杂系统的智能诊断。通过讨论分析了ID3算法的特点和作用。
RH-KTB vacuum system is used in secondary steelmaking. The basic principle and faults of RH-KTB are introduced. It is important for intelligent fault diagnosis to mined symptoms quickly and diagnose timely with selfstudy function. An example of induction ID3 algorithm is presented. The algorithm has self-study and self-organizing ability and is fit for intelligent diagnosis of complicated system. It was proved through the example that the proposed algorithm can be used for fault diagnosis of RH-KTB vacuum system. The characteristics and function of ID3 algorithm is analyzed.
出处
《钢铁研究学报》
CAS
CSCD
北大核心
2006年第4期59-62,共4页
Journal of Iron and Steel Research
基金
教育部博士基金资助项目(2000014520)
辽宁省科技基金重点资助项目(9910200102)
关键词
决策树
真空
智能
故障诊断
decision tree
vacuum
intelligence
fault diagnosis
