| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines |
| Autor/-in: | Hu, Yang Palmé, Thomas Fink, Olga |
| DOI: | 10.1016/j.engappai.2016.10.010 |
| Erschienen in: | Engineering Applications of Artificial Intelligence |
| Band(Heft): | 57 |
| Seite(n): | 105 |
| Seiten bis: | 117 |
| Erscheinungsdatum: | 2017 |
| Verlag / Hrsg. Institution: | Elsevier |
| ISSN: | 0952-1976 |
| Sprache: | Englisch |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | Early fault detection of engineering systems allows early warnings of anomalies and provides time to initiate proactive mitigation actions before the anomaly has developed to a problem that either requires extensive maintenance or affects the productivity of the system. In this paper, a new fault detection method using signal reconstruction based on Auto-Associative Extreme Learning Machines (AAELM) is proposed. AAELM are applied for fault detection on an artificially generated dataset to test the performance of the algorithm under controlled conditions and a real case study based on condition monitoring data from a combined-cycle power plant compressor. The performance of AAELM is compared to that of two other commonly used signal reconstruction methods: Auto-Associative Kernel Regression (AAKR) and Principal Component Analysis (PCA). The results from the two case studies demonstrate that AAELM achieve a smaller reconstruction error, shorter detection delay, lower spillover and a higher distinguishability compared to AAKR and PCA on the evaluated datasets. The obtained results are generalized to elaborate guidelines for industrial users for selecting suitable signal reconstruction algorithms based on their specific requirements and boundary conditions. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/13949 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
Es gibt keine Dateien zu dieser Ressource.
Zur Langanzeige
Hu, Y., Palmé, T., & Fink, O. (2017). Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines. Engineering Applications of Artificial Intelligence, 57, 105–117. https://doi.org/10.1016/j.engappai.2016.10.010
Hu, Y., Palmé, T. and Fink, O. (2017) ‘Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines’, Engineering Applications of Artificial Intelligence, 57, pp. 105–117. Available at: https://doi.org/10.1016/j.engappai.2016.10.010.
Y. Hu, T. Palmé, and O. Fink, “Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines,” Engineering Applications of Artificial Intelligence, vol. 57, pp. 105–117, 2017, doi: 10.1016/j.engappai.2016.10.010.
HU, Yang, Thomas PALMÉ und Olga FINK, 2017. Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines. Engineering Applications of Artificial Intelligence. 2017. Bd. 57, S. 105–117. DOI 10.1016/j.engappai.2016.10.010
Hu, Yang, Thomas Palmé, and Olga Fink. 2017. “Fault Detection Based on Signal Reconstruction with Auto-Associative Extreme Learning Machines.” Engineering Applications of Artificial Intelligence 57: 105–17. https://doi.org/10.1016/j.engappai.2016.10.010.
Hu, Yang, et al. “Fault Detection Based on Signal Reconstruction with Auto-Associative Extreme Learning Machines.” Engineering Applications of Artificial Intelligence, vol. 57, 2017, pp. 105–17, https://doi.org/10.1016/j.engappai.2016.10.010.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.