改进价值函数的多阶段间歇过程故障检测

Fault detection of multistage batch process based on improved value function

针对现有的阶段划分策略没有同时考虑间歇过程的动态性和多阶段特性,而导致过程检测效果不佳的问题,提出了一种改进的结合价值函数的多向核主成分分析(CVF-MKPCA)算法。首先,对间歇过程的三维数据进行相应的方向展开,并且通过构建扩展矩阵来提取间歇过程数据之间的动态特性;其次,构建一种改进的结合价值函数,评估互异时间序列信息之间的结构相似性;然后,根据动态结构相似性的评估要求,利用自下向上的搜索方法进行阶段划分,再采用MKPCA方法进行阶段建模,最后,通过构造出一种新的combine统计量对各阶段进行故障检测。所提算法在青霉素发酵仿真过程中故障误报率在控制限为95%时为3.40%,在控制限为99%时为7.98%,与对比方法相比误报率分别降低了2.12%和1.26%,证明了所提方法具有更优越的故障检测性能。

Aiming at the problem that the existing stage division strategy does not consider the dynamic and multi-stage characteristics of batch processes at the same time,resulting in poor process detection effect,an improved multi-phase kernel principal component analysis based on combined value function(CVF-MKPCA)algorithm is proposed.Firstly,the three-dimensional data of the batch process are expanded in the corresponding direction,and the dynamic characteristics between the data of the batch process are extracted by constructing the expansion matrix.Secondly,an improved combined value function is constructed to evaluate the structural similarity between different time series information;then,according to the evaluation requirements of dynamic structural similarity,the bottom-up search method is used for stage division,and MKPCA method is used for stage modeling.Finally,a new combine statistic is constructed to detect faults in each stage.In the simulation process of penicillin fermentation,the false alarm rate of the proposed algorithm is 3.40%when the control limit is 95%,and 7.98%when the control limit is 99%,compared with the comparison method,the false alarm rate is reduced by 2.12%and 1.26%respectively,which proves that the proposed method has better fault detection performance.