摘要
复杂过程故障预测是保证过程安全可靠运行的关键,而复杂系统的工作状态往往由多元时滞序列决定,该序列含有变量间的时滞信息及相关关系,具有一定的信息完备性。因此文章提出一种基于多元时滞序列驱动的复杂过程故障预测方法,该方法首先构建复杂系统的时滞符号有向图(TD-SDG)进而得到多元时滞序列,然后针对复杂系统变量多、关系复杂的问题,提出一种独立成分分析(ICA)和ELM神经网络集成的方法,此方法可快速获取多元时滞序列的独立成分从而建立监控统计量,最终达到故障预测的目的。通过在Tennessee Eastman(TE)过程上的仿真实验验证,表明所提方法能够至少提前15min预测到故障,方便工作人员及时有效地采取措施。
Complex process fault prognosis is a key scientific issue that ensures the security of the process and reliable operation, however complex systems work state is often determined by multivariate delayed sequence. It contains the relationship between the variables and the time delay information, so it has information completeness. So a complex process fault prognosis approach based on multivariate delayed sequences is proposed. First this method construct the Time Delay Signed Direct Digraph （TD-SDG） to get multi-delayed sequence, then combine Independent Component Analysis and ELM neural network to get the independent component of the multi-delayed sequence, and finally realize the purpose of fault prognosis of complex system. The simulation results on Tennessee Eastman process illustrate that the proposed method can predict fault earlier 15 min, increase operator＇s reaction time and detect the fault.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2013年第12期4290-4295,共6页
CIESC Journal
基金
国家自然科学基金项目(61104131)~~
关键词
故障预测
时滞
独立成分分析
ELM
TE过程
fault prognosis
time delay
independent component analysis
ELM
TE process
