Data-Driven Process Monitoring Using Structured Joint Sparse Canonical Correlation Analysis
Data-Driven Process Monitoring Using Structured Joint Sparse Canonical Correlation Analysis
复制标题
使用结构化联合稀疏典型相关分析进行数据驱动的过程监控
DOI：
10.1109/tcsii.2020.2988054
复制
发表时间：
2020
影响因子：
4.4
通讯作者：
Wanquan Liu
中科院分区：
文献类型：
--
作者：
Xianchao Xiu;Ying Yang;Lingchen Kong;Wanquan Liu
为了提高基于典型相关分析（CCA）的过程监控方法的性能，提出了一种基于结构化联合稀疏典型相关分析（SJSCCA）的过程监控方法.从技术上讲，图拉普拉斯算子可以包含结构化变量的相关性信息，而联合稀疏度可以丢弃无用的变量。所开发的两阶段交替方向法的乘数被证明是非常有效的，因为每个派生的子问题有一个封闭形式的解决方案，或可以解决快速求解器。为了检测异常情况，采用了$T^{2}$检验统计量。以Tennessee Eastman过程为例说明了SJSCCA的有效性。实验结果表明，与现有的基于CCA的方法相比，所提出的SJSCCA能够显著提高监测性能。
In order to improve the performance of canonical correlation analysis (CCA) based methods for process monitoring, this brief proposes a novel process monitoring approach using the structured joint sparse canonical correlation analysis (SJSCCA). Technically, the graph Laplacian could incorporate structured variable correlation information and the joint sparsity could discard useless variables. The developed two-stage alternating direction method of multipliers is shown to be very efficient because each derived subproblem has a closed-form solution or can be solved by fast solvers. In order to detect abnormal situations, $T^{2}$ test statistic is adopted. The validity of SJSCCA is illustrated by the benchmark Tennessee Eastman process. The achieved results show that the proposed SJSCCA is able to improve the monitoring performance significantly in comparison with the existing state-of-the-art CCA-based methods.