Journal ArticleDOI
Nonlinear process monitoring using kernel principal component analysis
TLDR
In this article, a new nonlinear process monitoring technique based on kernel principal component analysis (KPCA) is developed, which can efficiently compute principal components in high-dimensional feature spaces by means of integral operators and nonlinear kernel functions.About:
This article is published in Chemical Engineering Science.The article was published on 2004-01-01. It has received 927 citations till now. The article focuses on the topics: Kernel principal component analysis & Principal component analysis.read more
Citations
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Journal ArticleDOI
Review of Recent Research on Data-Based Process Monitoring
TL;DR: The natures of different industrial processes are revealed with their data characteristics analyzed and a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods.
Journal ArticleDOI
Data Mining and Analytics in the Process Industry: The Role of Machine Learning
TL;DR: The state-of-the-art of data mining and analytics are reviewed through eight unsupervisedLearning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms.
Journal ArticleDOI
Current status of machine prognostics in condition-based maintenance: a review
Ying Peng,Ming Dong,Ming J. Zuo +2 more
TL;DR: In this article, a review of recent literature that focuses on the machine prognostics has been reviewed, which can be classified into four categories: physical model, knowledge-based model, data-driven model, and combination model.
Journal ArticleDOI
Fault detection and diagnosis in process data using one-class support vector machines
Sankar Mahadevan,Sirish L. Shah +1 more
TL;DR: It is shown that the proposed algorithm outperformed PCA and DPCA both in terms of detection and diagnosis of faults.
Journal ArticleDOI
Applications of fault detection and diagnosis methods in nuclear power plants: A review
Jianping Ma,Jin Jiang,Jin Jiang +2 more
TL;DR: Popularity of FDD applications in NPPs will continuously increase as FDD theories advance and the safety and reliability requirement for NPP tightens.
References
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Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Journal ArticleDOI
Nonlinear component analysis as a kernel eigenvalue problem
TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Journal ArticleDOI
An introduction to kernel-based learning algorithms
TL;DR: This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods.
Journal ArticleDOI
Nonlinear principal component analysis using autoassociative neural networks
TL;DR: The NLPCA method is demonstrated using time-dependent, simulated batch reaction data and shows that it successfully reduces dimensionality and produces a feature space map resembling the actual distribution of the underlying system parameters.
Journal ArticleDOI
Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models
TL;DR: In this article, the rank estimation of the rank A of the matrix Y, i.e., the estimation of how much of the data y ik is signal and how much is noise, is considered.