[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124615-en":3,"doc-seo-124615-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124615,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Fault diagnosis in Aircraft Fuel System components with Machine learning algorithms - Doctor of Philosophy Thesis","Fault diagnosis for aircraft fuel system components addresses the need for higher reliability and safety as aircraft systems increase in design complexity. Faults can alter performance, cause operational downtime, and in worst cases lead to accidents. Condition-based maintenance relies on diagnostics and prognostics to support maintenance decisions using remaining useful life, yet few fuel-system monitoring solutions can detect and contain faults. The thesis develops data-driven fault detection using sensor information and machine learning on healthy and faulty data from an aircraft fuel system model.","CRANFIELD UNIVERSITY  \nNithya Subramanian  \nFault diagnosis in Aircraft Fuel System components with Machine  \nlearning algorithms  \nSCHOOL OF AEROSPACE, TRANSPORT AND MANUFACTURING  \nPhD Thesis Academic Year: 2018-2022  \nSupervisor: Prof Andrew Starr  \nAssociate Supervisor: Dr Suresh Perinpanayagam  \n01 2022  \nCRANFIELD UNIVERSITY  \nSCHOOL OF AEROSPACE, TRANSPORT AND MANUFACTURING  \nPhD Thesis  \nAcademic Year 2018-2022  \nNithya Subramanian  \nFault diagnosis in Aircraft Fuel System components with Machine  \nlearning algorithms  \nSupervisor: Prof Andrew Starr  \nAssociate Supervisor: Dr Suresh Perinpanayagam  \n01 2022  \nThis thesis is submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy  \n© Cranfield University 2022. All rights reserved. No part of this publication may be reproduced without the written permission of the  \ncopyright owner.  \nABSTRACT  \nThere is a high demand and interest in considering the social and environmental effects of the component’s lifespan. Aircraft are one of the most high-priced businesses that require the highest reliability and safety constraints. The complexity of aircraft systems designs also has advanced rapidly in the last decade. Consequently, fault detection, diagnosis and modification/ repair procedures are becoming more challenging. The presence of a fault within an aircraft system can result in changes to system performances and cause operational downtime or accidents in a worst-case scenario.  \nThe CBM method that predicts the state of the equipment based on data collected is widely used in aircraft MROs. CBM uses diagnostics and prognostics models to make decisions on appropriate maintenance actions based on the Remaining Useful Life (RUL) of the components  \nThe aircraft fuel system is a crucial system of aircraft, even a minor failure in the fuel system can affect the aircraft's safety greatly. A failure in the fuel system that impacts the ability to deliver fuel to the engine will have an immediate effect on system performance and safety. There are very few diagnostic systems that monitor the health of the fuel system and even fewer that can contain detected faults. The fuel system is crucial for the operation of the aircraft, in case of failure, the fuel in the aircraft will become unusable/unavailable to reach the destination.  \nIt is necessary to develop fault detection of the aircraft fuel system. The future aircraft fuel system must have the function of fault detection. Through the information of sensors and Machine Learning Techniques, the aircraft fuel system’s fault type can be detected in a timely manner.  \nThis thesis discusses the application of a Data-driven technique to analyse the healthy and faulty data collected using the aircraft fuel system model, which is similar to Boeing-777 . The data is collected is processed through Machine learning Techniques and the results are compared.  \nKeywords:  \nMachine learning, Diagnosis, PHM, CBM, Aircraft Fuel System, Component level  \nACKNOWLEDGEMENTS  \nUndertaking this PhD has been a truly life-changing experience for me, and it would not have been possible to do without the support and guidance that I received from many people.  \nMy sincerest thanks go to my supervisor Prof Andrew Starr and Dr Suresh Perinpanayagam for allowing me to work on this project and for giving so freely of their time, continuous advice, guidance, and support throughout the project.  \nI am grateful to my Amma (Meerabai) and Appa (Subramanian) for their love, patience, encouragement and support all my life. Thanks for putting me first in all your decisions big and small. I do not have any words to express how thankful and blessed I feel to be your daughter.  \nHasani Azamar, you are an intrinsic part of my Cranfield (academic and personal) life. It was a bumpy road, we got there at the end (with a lot of good memories). Thanks for patiently listening and answering all my academic and philosophical questions.  \nPhD researc","cbCaig1mkKvfReBq","https://ap.wps.com/l/cbCaig1mkKvfReBq","pdf",7666699,1,139,"English","en",105,"# Introduction\n## Research Background\n## Research Aim and objectives\n## Thesis Structure\n# Literature Review\n## Maintenance\n## Maintenance Evolution\n## Condition Based Maintenance\n### Data Acquisition\n### Signal Processing\n### Fault Diagnosis","[{\"question\":\"Why is fault diagnosis for the aircraft fuel system important?\",\"answer\":\"Fuel-system faults can strongly impact aircraft safety and performance by affecting the ability to deliver fuel to the engine, potentially making onboard fuel unusable. Immediate operational risks can include safety degradation and downtime.\"},{\"question\":\"How does the thesis relate fault diagnosis to CBM and PHM?\",\"answer\":\"The work is grounded in condition-based maintenance, which uses diagnostics and prognostics models to decide maintenance actions based on remaining useful life. It supports timely fault type detection using sensor data and machine learning techniques.\"},{\"question\":\"What data and method are used for the fault diagnosis approach?\",\"answer\":\"The thesis applies a data-driven technique to analyze healthy and faulty data collected using an aircraft fuel system model similar to Boeing-777. Machine learning is used to process the data and compare results across conditions.\"}]","Fault diagnosis in Aircraft Fuel System components with Machine learning algorithms - Doctor of Philosophy Thesis | PDF",1785893330,350,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"fault-diagnosis-in-aircraft-fuel-system-components-with-machine-learning-algorithms-doctor-of-philosophy-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/fault-diagnosis-in-aircraft-fuel-system-components-with-machine-learning-algorithms-doctor-of-philosophy-thesis/124615/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is fault diagnosis for the aircraft fuel system important?","Question",{"text":75,"@type":76},"Fuel-system faults can strongly impact aircraft safety and performance by affecting the ability to deliver fuel to the engine, potentially making onboard fuel unusable. Immediate operational risks can include safety degradation and downtime.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis relate fault diagnosis to CBM and PHM?",{"text":80,"@type":76},"The work is grounded in condition-based maintenance, which uses diagnostics and prognostics models to decide maintenance actions based on remaining useful life. It supports timely fault type detection using sensor data and machine learning techniques.",{"name":82,"@type":73,"acceptedAnswer":83},"What data and method are used for the fault diagnosis approach?",{"text":84,"@type":76},"The thesis applies a data-driven technique to analyze healthy and faulty data collected using an aircraft fuel system model similar to Boeing-777. 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