Abstract Considering the stringent requirements for product quality of complex industrial processes, the purpose of this study is to apply causality analysis to select causal features of quality-relevant variables; and then to improve the prediction performance and interpretability of inferential sensors. Based on the idea that low-dimensional causal features can approximate the underlying information of the process instead of the original high-dimensional measurements, feature causality analysis is proposed in this work. To describe dynamic information and extract efficient latent features, dynamic latent variable models are utilized to combine with feature causality analysis. After dynamic latent causal feature extraction, two kinds of inferential sensors are developed with extracted dynamic latent causal features. Several comparison studies have been implemented on the Tennessee Eastman benchmark process; the results show that the inferential sensors based on dynamic latent causal features obtain the best performance.

中文翻译：

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基于因果关系分析的数据驱动动态推理传感器

摘要 考虑到复杂工业过程对产品质量的严格要求，本研究的目的是应用因果关系分析来选择质量相关变量的因果特征；然后提高推理传感器的预测性能和可解释性。基于低维因果特征可以近似过程的底层信息而不是原始的高维测量的思想，本文提出了特征因果关系分析。为了描述动态信息并提取有效的潜在特征，利用动态潜在变量模型与特征因果关系分析相结合。在动态潜在因果特征提取之后，利用提取的动态潜在因果特征开发了两种推理传感器。已对田纳西伊士曼基准流程进行了多项比较研究；结果表明，基于动态潜在因果特征的推理传感器获得了最佳性能。