Kriging based Fault Detection and Diagnosis Approach for Nonlinear Noisy Dynamic Processes

Ahmed Shokry; Mohammad Hamed Ardakani; Gerard Escudero; Moisès Graells; Antonio Espuña

doi:10.1016/b978-0-444-63428-3.50014-x

ScienceGate Book Chapters

BOOK-CHAPTER

Kriging based Fault Detection and Diagnosis Approach for Nonlinear Noisy Dynamic Processes

Ahmed Shokry Mohammad Hamed Ardakani Gerard Escudero Moisès Graells Antonio Espuña

Year: 2016 Computer-aided chemical engineering/Computer aided chemical engineering Pages: 55-60 Publisher: Elsevier BV

DOI: 10.1016/b978-0-444-63428-3.50014-x

Get Full-Text PDF Get Analytical Report

Keywords:

Kriging Smoothing Fault detection and isolation Computer science Noise (video) Classifier (UML) Residual Data mining Nonlinear system Support vector machine Process (computing) Benchmark (surveying) Artificial intelligence Pattern recognition (psychology) Algorithm Machine learning

Metrics

Cited By

6.16

FWCI (Field Weighted Citation Impact)

Refs

0.98

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

Advanced Statistical Process Monitoring

Social Sciences → Decision Sciences → Statistics, Probability and Uncertainty

Advanced Control Systems Optimization

Physical Sciences → Engineering → Control and Systems Engineering

Kriging based Fault Detection and Diagnosis Approach for Nonlinear Noisy Dynamic Processes

Metrics

Citation History

Topics

Related Documents

Dynamic kriging based fault detection and diagnosis approach for nonlinear noisy dynamic processes

MK-DCCA Based Fault Diagnosis for Incipient Fault in Nonlinear Dynamic Processes

Fault detection and diagnosis of dynamic processes using weighted dynamic decentralized PCA approach

Fuzzy Observer-Based Fault Detection Design Approach for Nonlinear Processes

Fuzzy Model-Based Fault Detection Approach for Nonlinear Control Processes