Result: Wavelet kernel entropy component analysis with application to industrial process monitoring

Title:
Wavelet kernel entropy component analysis with application to industrial process monitoring
Authors:
YINGHUA YANG, XIAOLE LI, XIAOZHI LIU, XIAOBO CHEN
Source:
Neurocomputing (Amsterdam). 147:395-402
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 19 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie de l'information, Information theory, Genie mecanique. Construction mecanique, Mechanical engineering. Machine design, Métrologie industrielle. Contrôle, Industrial metrology. Testing, Généralités, General, Analyse composante principale, Principal component analysis, Análisis componente principal, Analyse régression, Regression analysis, Análisis regresión, Critère sélection, Selection criterion, Criterio selección, Détection défaut, Defect detection, Detección imperfección, Effet non linéaire, Non linear effect, Efecto no lineal, Entropie, Entropy, Entropía, Evaluation performance, Performance evaluation, Evaluación prestación, Extraction forme, Pattern extraction, Extracción forma, Industrie chimique, Chemical industry, Industria química, Modélisation, Modeling, Modelización, Monitorage, Monitoring, Monitoreo, Méthode noyau, Kernel method, Método núcleo, Problème valeur propre, Eigenvalue problem, Problema valor propio, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Théorie information, Information theory, Teoría información, Transformation ondelette, Wavelet transformation, Transformación ondita, Extraction caractéristique, Feature extraction, Extracción de características, Fault identification, Kernel entropy component analysis, Process monitoring, TE process, Wavelet transform
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China
ISSN:
0925-2312
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28836765
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mechanical engineering. Mechanical construction. Handling

Telecommunications and information theory
Accession Number:
edscal.28836765
Database:
PASCAL Archive

Further Information

Aiming at the features that modem industrial processes always have some characteristics of complexity and nonlinearity and the process data usually contain both Gaussion and non-Gaussion information at the same time, a new process performance monitoring and fault detection method based on wavelet transform and kernel entropy component analysis (WT-KECA) is proposed in this paper. Unlike other kernel feature extraction methods, this method chooses the best principal component vectors according to the maximal Renyi entropy rather than judging by the top eigenvalues and eigenvectors of the kernel matrix simply. Besides, it can denoise and anti-disturb due to the application of wavelet transform. The proposed method is applied to process monitoring in the Tennessee Eastman (TE) process and the fault identification is realized. The simulation results indicate that the proposed method is more feasible and efficient in comparing to KPCA method.