A Common and Individual Feature Extraction-Based Multimode Process Monitoring Method With Application to the Finishing Mill Process
A Common and Individual Feature Extraction-Based Multimode Process Monitoring Method With Application to the Finishing Mill Process
复制标题
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
DOI：
10.1109/tii.2018.2799600
复制
发表时间：
2018-11-01
影响因子：
12.3
通讯作者：
Dong, Jie
中科院分区：
文献类型：
--
作者：
Zhang, Kai;Peng, Kaixiang;Dong, Jie
本文提出了一种基于公共和个体（CnI）特征提取的过程监测（PM）方法，用于跟踪具有多种运行模式的过程的运行性能和产品质量。与传统方法不同，传统方法仅针对每种模式数据的个体特征分别开发PM模型，而新方法试图从所有模式数据中同时构建PM模型，包括获取捕捉不同模式背后公共特征的公共子空间，以及反映每种模式独特特征的个体子空间。新提出的框架是使用基于常规主成分分析（PCA）和偏最小二乘法（PLS）的方法实现的。由此产生的基于Cnl - PCA的运行性能监测方法和基于Cnl - PLS的产品质量监测方法被应用于典型的多模式精轧机过程（FMP），该过程中存在所有钢材产品的通用配置以及每种钢材的单独设置。最后，实际应用结果表明，所提出的方法在检测和识别多模式FMP中的不同故障方面可能更具优势。
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.