Comparison of different classification algorithms for fault detection and fault isolation in complex systems

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dc.contributor.authorJung, Marcel
dc.contributor.authorNiculita, Octavian
dc.contributor.authorSkaf, Zakwan
dc.date.accessioned2019-01-07T09:35:52Z
dc.date.available2019-01-07T09:35:52Z
dc.date.issued2018-02-08
dc.description.abstractDue to the lack of sufficient results seen in literature, feature extraction and classification methods of hydraulic systems appears to be somewhat challenging. This paper compares the performance of three classifiers (namely linear support vector machine (SVM), distance-weighted k-nearest neighbor (WKNN), and decision tree (DT) using data from optimized and non-optimized sensor set solutions. The algorithms are trained with known data and then tested with unknown data for different scenarios characterizing faults with different degrees of severity. This investigation is based solely on a data-driven approach and relies on data sets that are taken from experiments on the fuel system. The system that is used throughout this study is a typical fuel delivery system consisting of standard components such as a filter, pump, valve, nozzle, pipes, and two tanks. Running representative tests on a fuel system are problematic because of the time, cost, and reproduction constraints involved in capturing any significant degradation. Simulating significant degradation requires running over a considerable period; this cannot be reproduced quickly and is costly.en_UK
dc.identifier.citationMarcel Jung, Octavian Niculita and Zakwan Skaf. Comparison of different classification algorithms for fault detection and fault isolation in complex systems. Procedia Manufacturing, Volume 19, 2018, Pages 111-118en_UK
dc.identifier.issn2351-9789
dc.identifier.urihttps://doi.org/10.1016/j.promfg.2018.01.016
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/13783
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMaintenanceen_UK
dc.subjectMonitoringen_UK
dc.subjectMachine Learningen_UK
dc.titleComparison of different classification algorithms for fault detection and fault isolation in complex systemsen_UK
dc.typeArticleen_UK

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