Wang Xuemei, Wu Ping
School of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou, 310018, P. R. China.
ACS Omega. 2022 May 24;7(22):18904-18921. doi: 10.1021/acsomega.2c01892. eCollection 2022 Jun 7.
By considering autocorrelation among process data, canonical variate analysis (CVA) can noticeably enhance fault detection performance. To monitor nonlinear dynamic processes, a kernel CVA (KCVA) model was developed by performing CVA in the kernel space generated by kernel principal component analysis (KPCA). The Gaussian kernel is widely adopted in KPCA for nonlinear process monitoring. In Gaussian kernel-based process monitoring, a single learner is represented by a certain selected kernel bandwidth. However, the selection of kernel bandwidth plays a pivotal role in the performance of process monitoring. Usually, the kernel bandwidth is determined manually. In this paper, a novel ensemble kernel canonical variate analysis (EKCVA) method is developed by integrating ensemble learning and kernel canonical variate analysis. Compared to a single learner, the ensemble learning method usually achieves greatly superior generalization performance through the combination of multiple base learners. Inspired by the ensemble learning method, KCVA models are established by using different kernel bandwidths. Further, two widely used and monitoring statistics are constructed for each model. To improve process monitoring performance, these statistics are combined through Bayesian inference. A numerical example and two industrial benchmarks, the continuous stirred-tank reactor process and the Tennessee Eastman process, are carried out to demonstrate the superiority of the proposed method.
通过考虑过程数据之间的自相关，典型变量分析（CVA）可以显著提高故障检测性能。为了监测非线性动态过程，通过在核主成分分析（KPCA）生成的核空间中执行CVA，开发了一种核CVA（KCVA）模型。高斯核在基于KPCA的非线性过程监测中被广泛采用。在基于高斯核的过程监测中，单个学习器由某个选定的核带宽表示。然而，核带宽的选择对过程监测的性能起着关键作用。通常，核带宽是手动确定的。本文通过集成集成学习和核典型变量分析，开发了一种新颖的集成核典型变量分析（EKCVA）方法。与单个学习器相比，集成学习方法通常通过多个基学习器的组合实现大大优越的泛化性能。受集成学习方法的启发，使用不同的核带宽建立KCVA模型。此外，为每个模型构建了两个广泛使用的监测统计量。为了提高过程监测性能，通过贝叶斯推理将这些统计量进行组合。进行了一个数值例子和两个工业基准测试，即连续搅拌釜式反应器过程和田纳西伊士曼过程，以证明所提出方法的优越性。
