基于PARAFAC2时段划分的间歇过程故障检测

间歇过程的多时段特性直接影响多元统计分析过程建模的准确性。针对间歇过程多时段特性，本文提出一种基于平行因子分解2（PARAFAC2）时段划分的间歇过程故障检测方法，首先对每一个时间片矩阵进行PARAFAC2建模，得到时间片矩阵的模型控制限，然后从间歇过程初始时刻开始，按照时序依次将每个时间片添加到时间块并进行PARAFAC2建模，得到时间块矩阵的模型控制限，通过评估时间片和时间块模型控制限的差异性确定初始时段划分点，并利用时段评价划分指标（PPCI）获取最佳的时段划分结果，最后在所得结果基础上分别对各个时段构建MPCA故障检测模型，实现间歇过程故障检测。所提方法保留了间歇过程三维结构特征和数据的完整性，深入考虑了间歇过程实际运行的时序性，提高了故障检测的准确性。利用青霉素发酵过程仿真实验验证了所提方法的有效性。

Fault Detection of Multi-phase Batch Processes Based on PARAFAC2 Phase Partition

The multi-phases characteristics of the batch process directly influenced accuracy of multivariate statistical analysis process modeling. A fault detection method of multi-phases batch processes based on parallel factor analysis 2 (PARAFAC2) phase partition was presented for the multi-phases characteristics of the batch process. Firstly, a group of time-slice matrix models were built based on PARAFAC2, to get the control limits of time-slice matrices. Secondly, each time-slice matrix was added into the time-block matrices chronologically from the initial moment, and established models based on PARAFAC2 for the time-block matrices, to get the control limits of time-block matrices. Thirdly, the points of phase partition were found by evaluating the difference between the control limits of time-slice matrices and time-block matrices, then the optimal phase partition result was chosen according to the phase partition combination index(PPCI). Finally the MPCA models for each phase was built and the fault detection of batch process was realized. The proposed method preserved the three-way structural characteristics and data integrity of the batch process, considered chronological sequence in the actual operation of the batch process comprehensively, thus improved the accuracy of phase partition. The simulation experiments of penicillin fermentation process verified the effectiveness of the proposed method.