摘要
本文在传统符号有向图(SDG)方法的基础上,在SDG模型的节点都引入趋势基元思想,按时序过程顺序对化工生产过程数据进行实时趋势分析。将趋势明显、符合SDG方法处理的故障点进行寻源报警处理;将由生产调整、误差等引起的尖峰现象从故障检测中剔除;同时将实时数据运用绝对值差分累积和计算,对缓变故障和波动大、不稳定的变量有预防和警示作用。该系统方法使故障检测在运用SDG模型的因果关系的同时,更加有效地利用历史数据趋势。经过某石化厂PTA装置溶剂脱水塔实例分析,该方法使SDG的溯源范围更加有效,可避免干扰造成的系统扰动引起的误报,减少其多义性。
Based on the traditional SDG method,this paper introduce the trend element ideology on every node of the SDG model,and the chemical industry process data is used to instantly analyzing automatically in the time series order.The fault point with obvious trend and according with the SDG method will be searched the source of fault,spike phenomenon because of production adjustment and error will be removed from fault detection, furthermore absolute difference cumulation is used for prevention and reminding of the slow change fault and major fluctuations variable.One hand this method utilize the cause-and-effect relationship of the model,the other hand,it can make use of the history data information effectively.Based on the practice for the PTA solvent dehydration in some petrochemical factory,this method makes the process of tracing to the fault source more availability,avoid misinformation which brought by system disturbance,decrease ambiguity.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2010年第10期1375-1379,共5页
Computers and Applied Chemistry
基金
国家自然科学基金(60625302、20876044)
长江学者和创新团队发展计划资助(IRT0721)
上海市重点学科(B504)
973项目(2009CB320603)
国家高技术研究发展计划(863)(2008AA042902)
上海市科技攻关项目(08DZ1123100)资助.
关键词
定向符号图
趋势提取
故障诊断
差分累积
signed directed graph（SDG）
trend extract
fault diagnosis
difference cumulation
