Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis

Jicong Fan; Youqing Wang

doi:10.1016/j.ins.2013.06.021

ScienceGate Book Chapters

JOURNAL ARTICLE

Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis

Jicong Fan Youqing Wang

Year: 2013 Journal: Information Sciences Vol: 259 Pages: 369-379 Publisher: Elsevier BV

DOI: 10.1016/j.ins.2013.06.021

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Fault detection and isolation Kernel (algebra) Component (thermodynamics) Kernel principal component analysis Gaussian process Process (computing) Gaussian Independent component analysis Algorithm Kernel method Mathematics Artificial intelligence Support vector machine

Metrics

148

Cited By

7.81

FWCI (Field Weighted Citation Impact)

Refs

0.97

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Fault Detection and Control Systems

Physical Sciences → Engineering → Control and Systems Engineering

Spectroscopy and Chemometric Analyses

Physical Sciences → Chemistry → Analytical Chemistry

Mineral Processing and Grinding

Physical Sciences → Engineering → Mechanical Engineering

Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis

Metrics

Citation History

Topics

Related Documents

Fault Detection of Non‐Linear Processes Using Kernel Independent Component Analysis

Fault detection of multimode non-Gaussian dynamic process using dynamic Bayesian independent component analysis

Dynamic kernel independent component analysis approach for fault detection and diagnosis

Fault detection of nonlinear dynamic processes using dynamic kernel principal component analysis

Fault detection of non-Gaussian processes based on modified independent component analysis