Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis

Juan C. Tudón-Martínez; Ruben Morales-Menendez; Luis Garza-Castañón; Ricardo Ramirez-Mendoza

doi:10.1007/978-3-642-21822-4_2

Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis

Juan C. Tudón-Martínez, Ruben Morales-Menendez, Luis Garza-Castañón, Ricardo Ramirez-Mendoza

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Dynamic Principal Component Analysis (DPCA) and Artificial Neural Networks (ANN) are compared in the fault diagnosis task. Both approaches are process history based methods, which do not assume any form of model structure, and rely only on process historical data. Faults in sensors and actuators are implemented to compare the online performance of both approaches in terms of quick detection, isolability capacity and multiple faults identifiability. An industrial heat exchanger was the experimental test-bed system. Multiple faults in sensors can be isolated using an individual control chart generated by the principal components; the error of classification was 15.28% while ANN presented 4.34%. For faults in actuators, ANN showed instantaneous detection and 14.7% lower error classification. However, DPCA required a minor computational effort in the training step. © 2011 Springer-Verlag.

Original language	English
Title of host publication	Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis
Pages	10-18
Number of pages	9
ISBN (Electronic)	9783642218217
DOIs	https://doi.org/10.1007/978-3-642-21822-4_2
Publication status	Published - 25 Jul 2011
Externally published	Yes
Event	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - Duration: 25 Jul 2011 → …

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6703 LNAI
ISSN (Print)	0302-9743

Conference

Conference	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period	25/7/11 → …

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-21822-4_2

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79960512098&origin=inward

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Tudón-Martínez, J. C., Morales-Menendez, R., Garza-Castañón, L., & Ramirez-Mendoza, R. (2011). Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis. In Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis (pp. 10-18). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6703 LNAI). https://doi.org/10.1007/978-3-642-21822-4_2

Tudón-Martínez, Juan C. ; Morales-Menendez, Ruben ; Garza-Castañón, Luis ; Ramirez-Mendoza, Ricardo. / Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis. Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis. 2011. pp. 10-18 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis",

abstract = "Dynamic Principal Component Analysis (DPCA) and Artificial Neural Networks (ANN) are compared in the fault diagnosis task. Both approaches are process history based methods, which do not assume any form of model structure, and rely only on process historical data. Faults in sensors and actuators are implemented to compare the online performance of both approaches in terms of quick detection, isolability capacity and multiple faults identifiability. An industrial heat exchanger was the experimental test-bed system. Multiple faults in sensors can be isolated using an individual control chart generated by the principal components; the error of classification was 15.28% while ANN presented 4.34%. For faults in actuators, ANN showed instantaneous detection and 14.7% lower error classification. However, DPCA required a minor computational effort in the training step. {\textcopyright} 2011 Springer-Verlag.",

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Tudón-Martínez, JC, Morales-Menendez, R, Garza-Castañón, L & Ramirez-Mendoza, R 2011, Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis. in Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6703 LNAI, pp. 10-18, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 25/7/11. https://doi.org/10.1007/978-3-642-21822-4_2

Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis. / Tudón-Martínez, Juan C.; Morales-Menendez, Ruben; Garza-Castañón, Luis; Ramirez-Mendoza, Ricardo.

Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis. 2011. p. 10-18 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6703 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Dynamic Principal Component Analysis (DPCA) and Artificial Neural Networks (ANN) are compared in the fault diagnosis task. Both approaches are process history based methods, which do not assume any form of model structure, and rely only on process historical data. Faults in sensors and actuators are implemented to compare the online performance of both approaches in terms of quick detection, isolability capacity and multiple faults identifiability. An industrial heat exchanger was the experimental test-bed system. Multiple faults in sensors can be isolated using an individual control chart generated by the principal components; the error of classification was 15.28% while ANN presented 4.34%. For faults in actuators, ANN showed instantaneous detection and 14.7% lower error classification. However, DPCA required a minor computational effort in the training step. © 2011 Springer-Verlag.

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Tudón-Martínez JC, Morales-Menendez R, Garza-Castañón L, Ramirez-Mendoza R. Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis. In Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis. 2011. p. 10-18. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-21822-4_2