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dc.contributor.authorHu, Yang-
dc.contributor.authorPalmé, Thomas-
dc.contributor.authorFink, Olga-
dc.date.accessioned2018-12-18T15:24:24Z-
dc.date.available2018-12-18T15:24:24Z-
dc.date.issued2017-
dc.identifier.issn0952-1976de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13949-
dc.description.abstractEarly 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.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofEngineering Applications of Artificial Intelligencede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleFault detection based on signal reconstruction with Auto-Associative Extreme Learning Machinesde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1016/j.engappai.2016.10.010de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end117de_CH
zhaw.pages.start105de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume57de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
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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.


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