Extracting latent dynamics from process data for quality prediction and performance assessment via slow feature regression
Extracting latent dynamics from process data for quality prediction and performance assessment via slow feature regression
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DOI：
10.1109/acc.2015.7170850
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发表时间：
2015-07
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影响因子：
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通讯作者：
Chao Shang;Fan Yang;Xinqing Gao;Dexian Huang
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文献类型：
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作者：
Chao Shang;Fan Yang;Xinqing Gao;Dexian Huang
在过程控制问题中，特别是在质量预测任务中，潜变量（LV）模型如偏最小二乘（PLS）已被广泛用于推导低维子空间和建立回归模型。然而，它们基于工业过程在稳定状态下运行的假设，从而忽略了过程动态。本文提出了一种新的LV子空间线性回归模型-慢特征回归（SFR）。在第一步中，通过慢特征分析（SFA）提取慢特征作为LV，这是一种新兴的机器学习方法。与经典的LV模型不同，SFA假设LV具有缓慢变化的动态，可以通过分析大量过程数据中的时间结构来推导。由于工业过程中的明显动态性，慢度可以被认为是一个有效的先验知识来利用。在第二步中，选择最慢的特征作为过程的合理描述，以进一步预测产品质量，这也可能是缓慢变化的。除了Hotelling的T2统计量，提出了一种新的S2指数来评估过程内的动态变化和评估的预测模型的实时性能。基于SFR的方法的有效性通过在田纳西州伊士曼流程中的应用得到了证明。
Latent variable (LV) models such as partial least squares (PLS) have been widely used to derive low-dimensional subspaces and build regression models in process control problems, especially in quality prediction tasks. However, they are based on the assumption that industrial processes operate at steady states, thereby ignoring process dynamics. In this article, slow feature regression (SFR), a novel linear regression model with LV subspaces, is proposed, which consists of two steps. In the first step, slow features as LVs are extracted via slow feature analysis (SFA), a rising machine learning methodology. Different from classical LV models, SFA assumes LVs have slowly varying dynamics, which can be derived by analyzing the temporal structure within abundant process data. Owing to evident dynamics in industrial processes, slowness can be considered as a valid prior knowledge to utilize. In the second step, the slowest features are selected as a reasonable description of processes to further predict the product quality, which is also likely to be slowly varying. In addition to the Hotelling's T2 statistic, a novel S2 index is proposed to evaluate the dynamic variations within processes and assess the real-time performance of the prediction model. The effectiveness of the SFR-based approach is demonstrated through an application in the Tennessee Eastman process.