期刊
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
卷 96, 期 2, 页码 554-563出版社
WILEY
DOI: 10.1002/cjce.22938
关键词
nonlinear process monitoring; kernel learning; fault detection; fault identification
资金
- China's National 973 program [2009CB320600]
- NSF of China [61325015, 61273163]
This paper focuses on developing an advanced nonlinear process monitoring technique involving fault detection and identification methods. The new monitoring methods are proposed based on two nonlinear matrix factorization algorithms. Both factorizations use the kernel method to replace lower-dimensional nonlinearity using higher-dimensional linearity by nonlinearly mapping the data onto a high-dimensional linear space. In the high-dimensional linear space, also known as feature space, the first factorization decomposes the data matrix into two low-rank matrix products, in which the first matrix factor is restricted to being orthogonal and non-negative leading to a good performance in the subspace approximation of the original data. In the second factorization, a matrix consisting of all types of fault samples is decomposed into two low-rank matrix products, in which the second matrix factor is restricted to being orthogonal and non-negative providing a clear K-means clustering interpretation. On the basis of the above two factorizations, the corresponding fault detection and identification methods are developed. Finally, the proposed approaches are used to monitor the penicillin fermentation process (PFP), and encouraging experimental results are achieved.
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