Sparse canonical variate analysis approach for process monitoring
Sparse canonical variate analysis approach for process monitoring
复制标题
用于过程监控的稀疏典型变量分析方法
DOI：
10.1016/j.jprocont.2018.09.009
复制
发表时间：
2018-11-01
影响因子：
4.2
通讯作者：
Braatz, Richard D.
中科院分区：
文献类型：
--
作者：
Lu, Qiugang;Jiang, Benben;Braatz, Richard D.
典型变量分析（CVA）由于在处理高维、序列相关和交叉相关的动态数据方面的有效性，在统计过程监控中表现出优越性能。CVA的一个限制条件是因变量和自变量的协方差矩阵必须可逆，当过程变量之间存在共线性或样本量相对于变量数量较小时，这一条件可能不成立。此外，CVA通常会产生密集的典型向量，这阻碍了对过程变量之间潜在关系的解释。本文采用一种稀疏典型变量分析（SCVA）技术来解决这些问题，并将该方法应用于过程监控。提供了实施SCVA的详细算法及其在故障检测和识别中的公式。结果表明，SCVA有助于发现过程变量之间的主要结构（或关系），并通过汇总故障变量的贡献和抑制正常变量的贡献来辅助故障识别。所提方法的有效性在田纳西 - 伊斯曼过程中得到了验证。© 2018爱思唯尔有限公司。保留所有权利。
Canonical variate analysis (CVA) has shown its superior performance in statistical process monitoring due to its effectiveness in handling high-dimensional, serially, and cross-correlated dynamic data. A restrictive condition for CVA is that the covariance matrices of dependent and independent variables must be invertible, which may not hold when collinearity between process variables exists or the sample size is small relative to the number of variables. Moreover, CVA often yields dense canonical vectors that impede the interpretation of underlying relationships between the process variables. This article employs a sparse CVA (SCVA) technique to resolve these issues and applies the method to process monitoring. A detailed algorithm for implementing SCVA and its formulation in fault detection and identification are provided. SCVA is shown to facilitate the discovery of major structures (or relationships) among process variables, and assist in fault identification by aggregating the contributions from faulty variables and suppressing the contributions from normal variables. The effectiveness of the proposed approach is demonstrated on the Tennessee Eastman process. (C) 2018 Elsevier Ltd. All rights reserved.