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机构地区:[1]清华大学自动化系,北京100084
出 处:《计算机与应用化学》2010年第1期11-16,共6页Computers and Applied Chemistry
基 金:国家自然科学基金资助项目(NFSC40874049);国家高技术研究发展计划(863)资助项目(2007AA04Z193)
摘 要:基于模型的SDG(Signed DiGraph,符号有向图)故障诊断方法因其具有完备性好、推理深度高等优点在过程工业安全工程中具有十分重要的意义,已成为安全工程中的1种关键技术。本文在以前研究的基础上,提出了1个综合性SDG故障诊断架构,以期能够实现在生产过程中及时发现故障并判明故障源。该综合性故障诊断架构按模型、推理和应用3个层次搭建,以传统定性SDG及概率SDG理论为基础,包含了从模型建立到故障诊断推理,从定性SDG方法到结合统计监控的SDG方法再到概率SDG方法等一系列实施方案。该综合性SDG故障诊断架构由于引入了多元统计监控模型,使得在系统没有表现出明显的故障征兆时就能够及时敏感地检测到异常变化,进而触发SDG及PSDG推理来实现对故障源的查找,并给出各故障源发生故障的概率值,以指导使用者按照概率值的大小顺序采取处理措施。以某石化公司的气体分馏装置为实际背景,利用该装置实时数据库中的实际生产工艺数据对该综合性诊断架构进行了实例验证,其故障诊断结果与实际发生的故障相吻合,证明了该综合性故障诊断架构的有效性。SDG is one of the model-based methods for fault diagnosis.It has good maturity,in-depth reasoning ability and several other advantages,so that has a great significance in safety engineering of process industry.In this paper,an integrative framework for SDG based fault diagnosis is proposed to achieve the detection of fault symptoms and find out the fault origins as soon as possible in process industry.This framework,based on the theories of traditional qualitative SDG and probabilistic SDG,includes the procedure from SDG modeling to fault diagnosis inference and a series of methods including the qualitative SDG method,the hybrid method combining SDG with multivariable statistical process monitoring and the probabilistic SDG method.By introducing the multivariate statistical monitoring models,the framework can detect abnormal changes of the system sensitively and duly before the distinct symptoms emerge.And then the SDG and PSDG inference can be triggered to search the fault origins,also,the probabilities of the candidate faults can be calculated to guide the user to take measures according to the order of probabilities.By this framework,the entire theoretical framework and application system for SDG are constructed and enriched in modeling,inference and application levels.The framework is validated through a fault diagnosis case on the gas fractionation unit of a certain petrochemical company.
分 类 号:TP277[自动化与计算机技术—检测技术与自动化装置] TP391.9[自动化与计算机技术—控制科学与工程]
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