期刊
SYSTEMS SCIENCE & CONTROL ENGINEERING
卷 4, 期 1, 页码 165-174出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/21642583.2016.1198940
关键词
Fault detection and identification; process monitoring; nonlinear systems; multivariatestatistics; kernel density estimation
资金
- Bayelsa State Scholarship Board (Government of Bayelsa State of Nigeria) [BSSB/AD/CON/VOL.1 PHD.006]
Kernel principal component analysis (KPCA) is an effective and efficient technique for monitoring nonlinear processes. However, associating it with upper control limits (UCLs) based on the Gaussian distribution can deteriorate its performance. In this paper, the kernel density estimation (KDE) technique was used to estimate UCLs for KPCA-based nonlinear process monitoring. The monitoring performance of the resulting KPCA-KDE approach was then compared with KPCA, whose UCLs were based on the Gaussian distribution. Tests on the Tennessee Eastman process show that KPCA-KDE is more robust and provide better overall performance than KPCA with Gaussian assumption-based UCLs in both sensitivity and detection time. An efficient KPCA-KDE-based fault identification approach using complex step differentiation is also proposed.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据