A clustering approach to detect faults with multi-component degradations in aircraft fuel systems

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dc.contributor.authorZaporowska, Anna
dc.contributor.authorLiu, Haochen
dc.contributor.authorSkaf, Zakwan
dc.contributor.authorZhao, Yifan
dc.date.accessioned2021-06-22T12:58:35Z
dc.date.available2021-06-22T12:58:35Z
dc.date.issued2020-12-18
dc.description.abstractAccurate fault diagnosis and prognosis can significantly increase the safety and reliability of engineering systems and also reduce the maintenance costs. There is very limited relative research reported on the fault diagnosis of a complex system with multi-component degradation. The Complex Systems (CS) problem, which features multiple components simultaneously and nonlinearly interacting with each other and corresponding environment on multiple levels, has become an essential challenge in system engineering. In CS, even a single component degradation could cause misidentification of the fault severity level and lead to serious consequences. This paper introduces a new test rig to simulate multi-component degradations of the aircraft fuel system. A data analysis approach based on machine learning classification of both the time and frequency domain features is then proposed to detect and identify the fault severity level of CS with multi-component degradation. Results show that a) the fault can be sensitively detected with an accuracy > 99%; b) the severity of fault can be identified with an accuracy of 100%.en_UK
dc.identifier.citationZaporowska A, Liu H, Skaf Z, Zhao Y. (2020) A clustering approach to detect faults with multi-component degradations in aircraft fuel systems. IFAC-PapersOnLine, Volume 53, Issue 3, pp. 113-118en_UK
dc.identifier.issn2405-8963
dc.identifier.urihttps://doi.org/10.1016/j.ifacol.2020.11.018
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16803
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.subjectFast Fourier Transformen_UK
dc.subjectClustering analysisen_UK
dc.subjectK-means clusteringen_UK
dc.subjectFault Diagnosisen_UK
dc.titleA clustering approach to detect faults with multi-component degradations in aircraft fuel systemsen_UK
dc.typeArticleen_UK

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