摘要
针对赤铁矿石竖炉焙烧过程故障诊断问题,将专家系统、神经网络与案例推理技术相结合,提出了由故障预报、质量监督、故障分析与处理模块构成的竖炉焙烧过程智能故障诊断系统。讨论了系统的体系结构、功能及实现方法,并成功应用于中国最大的赤铁矿选矿厂的竖炉焙烧过程。经过现场运行,故障诊断准确率达到90%,设备运转率提高了2．98%。工业应用结果表明,本文提出的智能故障诊断系统对竖炉焙烧这类复杂工业过程的故障诊断可行。
Combined with expert system, neural networks and case-based reasoning technology, an intelligent fault diagnosis system is established for the roasting process of the shaft furnace in hematite ores processing. The structure, the function and realization methods of the proposed system are presented. The system is applied to the roasting process of the shaft furnace in the biggest hematite ores processing factory in China. After the practical implementation, the accuracy rate of the fault diagnosis reaches 90% and the equipment operating rate is increased by 2.98%. Industrial application shows that the proposed system is feasible for the fault diagnosis project of complex industrial processes as the roasting process of shaft furnace.
出处
《南京航空航天大学学报》
EI
CAS
CSCD
北大核心
2006年第B07期91-94,共4页
Journal of Nanjing University of Aeronautics & Astronautics
基金
国家"九七三"重点基础研究发展计划(2002CB312201)资助项目
国家自然科学基金(60534010)资助项目
国家创新研究群体科学基金(60521003)资助项目
长江学者和创新团队发展计划(IRT0421)资助项目
关键词
故障诊断
竖炉焙烧
神经网络
专家系统
案例推理
fault diagnosis
roasting process of shaft furnace
neural network
expert system
casebased reasoning
