This paper proposes a common and individual (CnI) feature extraction-based process monitoring (PM) method for tracking the operating performance and product quality of processes with multiple operating modes. Different from traditional methods that separately develop PM models concerning only the individual feature of each mode data, the new method seeks to build the PM model simultaneously from all mode data, including to acquire the common subspace that captures the common feature behind different modes, and the individual subspace that reflects the unique feature of each mode. The newly proposed framework is achieved using the conventional principal component analysis (PCA) and partial least squares (PLS) based methods. The resulting CnI-PCA-based operating performance monitoring method and CnI-PLS-based product quality monitoring method are applied to the typical multimode finishing mill process (FMP) where common configuration for all steel products and individual setting for each steel are existing. Finally, the practical application result shows that the proposed method can be preferable to detect and identify different faults in the multimode FMP.

中文翻译：

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基于共性和个体特征提取的多模式过程监控方法及其在精轧过程中的应用


本文提出了一种基于公共和个体（CnI）特征提取的过程监控（PM）方法，用于跟踪具有多种操作模式的过程的操作性能和产品质量。与仅考虑每个模式数据的单独特征单独开发 PM 模型的传统方法不同，新方法寻求从所有模式数据同时构建 PM 模型，包括获取捕获不同模式背后的共同特征的公共子空间，以及反映每种模式独特特征的单独子空间。新提出的框架是使用传统的主成分分析（PCA）和偏最小二乘（PLS）方法实现的。由此产生的基于 CnI-PCA 的运行性能监控方法和基于 CnI-PLS 的产品质量监控方法适用于典型的多模式精轧工艺 (FMP)，其中存在所有钢材产品的通用配置和每种钢材的单独设置。最后，实际应用结果表明，该方法能够较好地检测和识别多模FMP中的不同故障。