Dynamic statistical process monitoring methods have been widely studied and applied in modern industrial processes. These methods aim to extract the most predictable temporal information and develop the corresponding dynamic monitoring schemes. However, measurement noise is widespread in real-world industrial processes, and ignoring its effect will lead to sub-optimal modeling and monitoring performance. In this article, a probabilistic predictable feature analysis (PPFA) is proposed for multivariate time series modeling, and a multi-step dynamic predictive monitoring scheme is developed. The model parameters are estimated with an efficient expectation-maximum algorithm, where the genetic algorithm and Kalman filter are designed and incorporated. Further, a novel dynamic statistical monitoring index, Dynamic Index, is proposed as an important supplement of $\text{T}^2$ and $\text{SPE}$ to detect dynamic anomalies. The effectiveness of the proposed algorithm is demonstrated via its application on the three-phase flow facility and a medium speed coal mill.
翻译:对动态统计过程监测方法进行了广泛研究,并应用于现代工业过程,这些方法旨在提取最可预测的时间信息,并制定相应的动态监测计划;然而,计量噪音在现实世界工业过程中十分普遍,忽视其影响将导致低于最佳的建模和监测性能;在本条中,提议对多变时间序列建模进行概率可预见特征分析,并制订多步骤动态监测计划;模型参数以高效的预期最大算法估算,其中基因算法和Kalman过滤器是设计和纳入的;此外,还提出了新的动态统计监测指数,即动态指数,作为美元/T ⁇ 2美元和美元/text{SPE}美元的重要补充,用以检测动态异常;拟议的算法的有效性通过在三阶段流动设施和中速煤厂的应用得到证明。