Abstract
| 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 | |
| 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 → … |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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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
TY - GEN
T1 - Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis
AU - Tudón-Martínez, Juan C.
AU - Morales-Menendez, Ruben
AU - Garza-Castañón, Luis
AU - Ramirez-Mendoza, Ricardo
PY - 2011/7/25
Y1 - 2011/7/25
N2 - 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.
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.
U2 - 10.1007/978-3-642-21822-4_2
DO - 10.1007/978-3-642-21822-4_2
M3 - Conference contribution
SN - 9783642218217
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 10
EP - 18
BT - Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis
ER -