基于混合分块DMICA-PCA的全流程过程监控方法

分块策略被广泛运用于全流程过程监控领域，以解决全流程过程变量关系复杂性较高的问题，但传统的分块策略与子块建模方法都未考虑过程的动态性问题，并且传统的分块策略都片面依赖于过程知识或过程数据信息，影响了过程监控的效果，为此提出了一种基于混合分块DMICA-PCA的过程监控方法。在分析过程的动态性后，先利用已知的部分过程知识进行变量的初步分块，接着利用各分块变量之间改进的广义Dice's系数（MGDC）进行进一步的分块。然后采用DMICA-PCA方法对每个子块进行建模得到子块的统计量，并通过加权方法得到总的联合指标进行故障检测。同时对每个子块采用改进的故障诊断方法，提高了诊断效果。最后将该方法应用在TE过程的过程监控中，证明了该方法的有效性。

Plant-wide process monitoring based on mixed multiblock DMICA-PCA

Multiblock strategy is widely used in plant-wide process monitoring to solve problems with complicated relationships between process variables. Traditional multiblock strategies and sub-block modeling methods are not effective in plant-wide process monitoring, because dynamic characteristics of the process have not been considered and knowledge or data information of the process is exclusively exploited. A mixed multiblock DMICA-PCA method was proposed to improve process monitoring performance. First, variables were sliced into initial sub-blocks by obtained process knowledge after analysis of process dynamics and further sliced into final sub-blocks by modified general Dice's coefficient (MGDC) between variables of initial sub-blocks. Then, the DMICA-PCA method was used to establish model and acquire statistical values of variables in final sub-blocks and a combined overall index from weighted sum was developed for fault detection, which improved performances by simultaneous diagnosis on each sub-block. Effectiveness of the proposed method was validated on monitoring the Tennessee-Eastman (TE) process.