Intelligent fault diagnosis of an aircraft fuel system using machine learning - a literature review

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dc.contributor.authorLi, Jiajin
dc.contributor.authorKing, Stephen
dc.contributor.authorJennions, Ian
dc.date.accessioned2023-04-20T12:02:45Z
dc.date.available2023-04-20T12:02:45Z
dc.date.issued2023-04-16
dc.description.abstractThe fuel system, which aims to provide sufficient fuel to the engine to maintain thrust and power, is one of the most critical systems in the aircraft. However, possible degradation modes, such as leakage and blockage, can lead to component failure, affect performance, and even cause serious accidents. As an advanced maintenance strategy, Condition Based Maintenance (CBM) can provide effective coverage, by combining state-of-the-art sensors with data acquisition and analysis techniques to guide maintenance before the asset’s degradation becomes serious. Artificial Intelligence (AI), particularly machine learning (ML), has proved effective in supporting CBM, for analyzing data and generating predictions regarding the asset’s health condition, thus influencing maintenance plans. However, from an engineering perspective, the output of ML algorithms, usually in the form of data-driven neural networks, has come into question in practice, as it can be non-intuitive and lacks the ability to provide unambiguous engineering signals to maintainers, making it difficult to trust. Engineers are interested in a deterministic decision-making process and how it is being revealed; algorithms should be able to certify and convince engineers to approve recommended actions. Explainable AI (XAI) has emerged as a potential solution, providing some of the logic on how the output is derived from the input given, which may help users understand the diagnostic result of the algorithm. In order to inspire and advise data scientists and engineers who are about to develop and use AI approaches in fuel systems, this paper explores the literature of experiment, simulation, and AI-based diagnostics for the fuel system to make an informed statement as to the progress that has been made in intelligent fault diagnostics for fuel systems, emphasizing the necessity of giving unambiguous engineering signals to maintainers, as well as highlighting potential areas for future research.en_UK
dc.identifier.citationLi J, King S, Jennions I. (2023) Intelligent fault diagnosis of an aircraft fuel system using machine learning - a literature review, Machines, Volume 11, Issue 4, April 2023, Article number 481en_UK
dc.identifier.issn2075-1702
dc.identifier.urihttps://doi.org/10.3390/machines11040481
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19519
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectBoeing 777en_UK
dc.subjectfuel systemen_UK
dc.subjectexperimenten_UK
dc.subjectsimulation modellingen_UK
dc.subjectcondition-based maintenanceen_UK
dc.subjectfault detection and diagnosticsen_UK
dc.subjectartificial intelligenceen_UK
dc.subjectreasoningen_UK
dc.subjectmachine learningen_UK
dc.subjectXAIen_UK
dc.titleIntelligent fault diagnosis of an aircraft fuel system using machine learning - a literature reviewen_UK
dc.typeArticleen_UK

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