A machine learning-based clustering approach to diagnose multi-component degradation of aircraft fuel systems

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dc.contributor.authorLiu, Haochen
dc.contributor.authorZhao, Yifan
dc.contributor.authorZaporowska, Anna
dc.contributor.authorSkaf, Zakwan
dc.date.accessioned2021-10-08T13:33:12Z
dc.date.available2021-10-08T13:33:12Z
dc.date.issued2021-10-07
dc.description.abstractAccurate fault diagnosis and prognosis can significantly reduce maintenance costs, increase the safety and availability of engineering systems that have become increasingly complex. It has been observed that very limited researches have been reported on fault diagnosis where multi-component degradation are presented. This is essentially a challenging Complex Systems problem where features multiple components interacting simultaneously and nonlinearly with each other and its environment on multiple levels. Even the degradation of a single component can lead to a misidentification of the fault severity level. This paper introduces a new test rig to simulate the multi-component degradation of the aircraft fuel system. A machine learning-based data analytical approach based on the classification of clustering features from both time and frequency domains is proposed. The scope of this framework is the identification of the location and severity of not only the system fault but also the multi-component degradation. The results illustrate that (a) the fault can be detected with accuracy > 99%; (b) the severity of fault can be identified with an accuracy of almost 100%; (c) the degradation level can be successfully identified with the R-square value > 0.9.en_UK
dc.identifier.citationLiu H, Zhao Y, Zaporowska A, Skaf Z. (2021) A machine learning-based clustering approach to diagnose multi-component degradation of aircraft fuel systems. Neural Computing and Applications, Available online 7 October 2021en_UK
dc.identifier.issn0941-0643
dc.identifier.urihttps://doi.org/10.1007/s00521-021-06531-4
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17154
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectFast Fourier transformen_UK
dc.subjectClustering analysisen_UK
dc.subjectFault diagnosisen_UK
dc.subjectMulti-component degradationen_UK
dc.titleA machine learning-based clustering approach to diagnose multi-component degradation of aircraft fuel systemsen_UK
dc.typeArticleen_UK

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