In recent decades, soft sensors have been profoundly studied and successfully applied to predict critical process variables in real‐time. While dealing with various application scenarios, data‐driven methods with representation learning possess great potentials. Latent features are formulated in these approaches to predict outputs from correlated input variables. In this study, a probabilistic framework of feature extraction is proposed in the context of process data analysis. To address switching behaviors in industrial processes, multiple emission models are utilized to construct latent space. To address temporal correlations from continuously operating processes, a dynamic model is implemented in latent space. Bayesian learning strategy is then developed for parameters estimation, where modeling preferences and uncertainties from multiple models are considered. The effectiveness and practicability of the proposed feature extraction algorithm are illustrated through numerical simulations, as well as an industrial case study. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2037–2051, 2018

中文翻译：

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通过切换模型提取动态特征，以进行过程数据分析并将其应用于软感测

近几十年来，软传感器已经得到了深入的研究，并成功地用于实时预测关键过程变量。在处理各种应用场景时，具有表示学习的数据驱动方法具有巨大的潜力。在这些方法中制定了潜在特征，以根据相关的输入变量预测输出。在这项研究中，在过程数据分析的背景下提出了特征提取的概率框架。为了解决工业过程中的转换行为，利用多种排放模型来构造潜在空间。为了解决来自连续运行过程的时间相关性，在潜在空间中实现了动态模型。然后开发贝叶斯学习策略以进行参数估计，其中考虑了来自多个模型的建模偏好和不确定性。通过数值仿真和工业案例研究，说明了所提特征提取算法的有效性和实用性。©2018美国化学工程师学会AIChE J，64：2037–2051，2018