面向工业混杂系统故障检测的扩展数据逻辑分析方法

An extended logical analysis of data approach to fault detections of industrial hybrid systems

常规的数据驱动故障检测方法难以处理同时包含连续和离散变量的工业混杂系统,数据逻辑分析(logicalanalysisofdata,LAD)方法通过对历史数据中变量组合的逻辑分析,能够有效地挖掘离散和连续变量数据中存在的隐含规则。然而,常规的LAD在提取连续变量特征时存在对趋势变化信息丢失的问题,并且在处理具有高维度、多变量特征的工业数据时会导致提取的规则存在大量冗余。为此,本文提出一种基于扩展数据逻辑分析(extendedlogicalanalysisofdata,ELAD)的工业混杂系统故障检测方法,根据与关键变量的关联度选取相关变量,增加变量的趋势信息以进行过程状态变化的表征,生成可解释的故障检测模型。应用于工业煤气化汽包过程,有效地检测了关键混杂变量对汽包液位故障的影响,实验结果验证了所提方法的可行性和有效性。

It is difficult to deal with industrial hybrid systems involving both continuous and discrete variables using conventional data-driven fault detection methods.While logical analysis of data(LAD)methods are able to effectively explore hidden rules in discrete and continuous data by means of logical analysis for variable associations.However,conventional LAD has the problem of losing trend change information when extracting features of continuous variables.And when processing industrial data with high-dimensional,multivariate features,it will cause a lot of redundancy in the extracted rules.Motivated by these observations,this paper presents an extended logical analysis of data(ELAD)approach to fault detections of industrial hybrid systems.Therein,correlated variables are selected according to the association degree with key variables and additive variable trends are employed to characterize process status changes,creating an explicable fault detection model.The proposed method is applied to the steam drum process of an industrial coal gasification plant in detecting the influence of key hybrid variables on the fault of steam drum level.The results verify the feasibility and effectiveness of the contribution.