A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics

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dc.contributor.authorZhao, Junjie
dc.contributor.authorLi, Yi-Guang
dc.contributor.authorSampath, Suresh
dc.date.accessioned2023-01-16T12:53:03Z
dc.date.available2023-01-16T12:53:03Z
dc.date.issued2022-12-23
dc.description.abstractFuture trends in engine health management (EHM) systems are information fusion, advanced analytical methods, and the concept of the Intelligent Engines. Machine Learning (ML)-based aero-engine gas path diagnostic methods are promising under the motivation of these trends. However, previous ML-based diagnostic structures are rarely applied in actual engineering practice because they are purely mathematical and lack physical insight or are limited by the error accumulation problem. Developing an accurate, flexible and interpretable intelligent diagnostic method has always posed a challenge, especially when physical knowledge is also available for more diagnostic information. Instead of modifying and applying existing ML methods for classification or regression, this study proposes a novel hierarchical diagnostic method to get insight into the physical systems, build hierarchies automatically, and recommend the classification structures. The proposed hierarchical diagnostic method is evaluated against a NASA model high-bypass two-spool turbofan engine. NASA's blind test case results show that Kappa Coefficient of the proposed hierarchical diagnostic method is 0.693 and is at least 0.008 higher than the other diagnostic methods in the open literature. It has been proved that the proposed method can quantify the dependence relationships between the fault classes for enhanced diagnostic information, recommend the best diagnostic structure for reduced complexity, and solve the error accumulation problem for improved diagnostic accuracy. The proposed method could support intelligent condition monitoring systems by effectively exploiting physical and data-based information for improved model interpretability, model flexibility, diagnostic visibility, diagnostic accuracy, and diagnostic reliability.en_UK
dc.identifier.citationZhao J, Li Y-G, Sampath S. (2023) A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics, Applied Energy, Volume 332, February 2023, Article number 120520en_UK
dc.identifier.eissn1872-9118
dc.identifier.issn0306-2619
dc.identifier.urihttps://doi.org/10.1016/j.apenergy.2022.120520
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18963
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAero enginesen_UK
dc.subjectIntelligent condition monitoringen_UK
dc.subjectGas path diagnosticsen_UK
dc.subjectMachine Learningen_UK
dc.subjectHierarchical diagnostic methoden_UK
dc.titleA hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnosticsen_UK
dc.typeArticleen_UK

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