[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122819-en":3,"doc-seo-122819-105":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},122819,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Advancing Fault Diagnosis in Aircraft Landing Gear - An Innovative Two-Tier Machine Learning Approach - with Intelligent Sensor Data Management","Revolutionizing aircraft safety, the study presents a two-tier machine learning framework for real-time fault diagnosis in aircraft landing gear systems. It tackles a critical limitation of traditional methods: sensor data anomalies that degrade diagnostic reliability. The approach models complex hydraulic failures using simulation-based data to train robust classifiers while a secondary tier imputes and corrects irregular sensor inputs through redundant measurements. Results show diagnostic accuracy improves from 95.88% to 98.76% after imputation, enabling uninterrupted health assessment and supporting safer, more cost-effective maintenance decisions.","AIAA SciTech Forum 10.2514/6 .2024-0759  \n8-12 January 2024, Orlando, FL AIAA SCITECH 2024 Forum  \nAdvancing Fault Diagnosis in Aircraft Landing Gear: An innovative two-tier Machine Learning Approach with intelligent sensor data management  \nKadripathi KN, Adolfo Perrusquia, Antonios Tsourdos, Dmitry Ignatyev School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 OAL,  \nUnited Kingdom  \nRevolutionizing aircraft safety, this study unveils a pioneering two-tier machine learning model specifically designed for advanced fault diagnosis in aircraft landing gear systems.  \nAddressing the critical gap in traditional diagnostic methods, our approach deftly navigates  \nthe challenges of sensor data anomalies, ensuring robust and accurate real-time health  \nassessments. This innovation not only promises to enhance the reliability and safety of aviation  \nbut also sets a new benchmark in the application of intelligent machine-learning solutions in  \nhigh-stakes environments. Our method is adept at identifying and compensating for data  \nanomalies caused by faulty or uncalibrated sensors, ensuring uninterrupted health  \nassessment. The model employs a simulation-based dataset reflecting complex hydraulic  \nfailures to train robust machine learning classifiers for fault detection. The primary tier  \nfocuses on fault classification, whereas the secondary tier corrects sensor data irregularities,  \nleveraging redundant sensor inputs to bolster diagnostic precision. Such integration markedly  \nimproves classification accuracy, with empirical evidence showing an increase from 95.88%  \nto 98.76% post-imputation. Our findings also underscore the importance of specific sensors—  \nparticularly temperature and pump speed—in evaluating the health of landing gear,  \nadvocating for their prioritized usage in monitoring systems. This approach promises to  \nrevolutionize maintenance protocols, reduce operational costs, and significantly enhance the  \nsafety measures within the aviation industry, promoting a more resilient and data-informed  \nsafety infrastructure.  \nKeywords: Fault Diagnosis, Aircraft Landing Gear Systems, Machine Learning, Sensor Data Imputation, Hydraulic Failure Simulation, Safety Enhancement in Aviation, Real-time Health Assessment, Diagnostic Accuracy Improvement.  \nI. Nomenclature  \nAI = Artificial Intelligence  \nML = Machine Learning  \nICAO = International Civil Aviation Organization  \nATA 32 = Air Transport Association Chapter 32: Landing Gear  \nTapAir = Hydraulic fluid-to-air ratio  \nRPM = Revolutions Per Minute  \nEDA = Exploratory Data Analysis  \nKNN = K-Nearest Neighbors  \nXGBoost = Extreme Gradient Boosting  \n1  \nCopyright © 2024 by Kadripathi KN. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.  \nIssued with: Creative Commons Attribution License (CC:BY 4.0) . The final published version (version of record) is available online at DOI:10.2514/6.2024-0759 . Please refer to any applicable publisher terms of use.  \nII. Introduction  \nAircraft, epitomizing the zenith of modern engineering, comprise an intricate matrix of systems and subsystems functioning cohesively to guarantee a secure flight. At the heart of this matrix lies the landing gear system, an indispensable, non-redundant element of an aircraft's architecture. Acting as a conduit between the aircraft and the ground, it encompasses a range of dynamic components, including the landing gear, wheels, brakes, shock absorbers, retraction mechanisms, control valves, and supplementary systems. Notably, each component within an aircraft is usually complemented by a backup or redundant system to ensure continuity of operation in the event ofa malfunction. For instance, should a primary thruster fail, the aircraft can seamlessly switch to an auxiliary thruster, enabling the flight to proceed to the nearest suitable landing site. This redundancy principle applies to various subsystems, such as navigation, ","cbCaij3KPy1LU3y4","https://ap.wps.com/l/cbCaij3KPy1LU3y4","pdf",843661,1,15,"English","en",105,"# Introduction\n## Landing gear health monitoring: safety and economic impact\n## Limits of model-driven vs data-driven diagnostic methods\n## Motivation for two-tier robust ML with sensor anomaly handling","[{\"question\":\"Why is landing gear health monitoring critical for aviation?\",\"answer\":\"Landing gear health monitoring directly supports passenger safety and economic efficiency. The document cites a significant share of aviation accidents linked to landing gear failure.\"},{\"question\":\"What problem does the proposed two-tier method address in fault diagnosis?\",\"answer\":\"It addresses sensor data anomalies that can mislead machine learning models, causing false alarms or missed/incorrect fault predictions.\"},{\"question\":\"How does the two-tier model improve diagnostic accuracy?\",\"answer\":\"The primary tier focuses on fault classification, while the secondary tier corrects sensor irregularities using redundant sensor inputs, improving accuracy from 95.88% to 98.76% after imputation.\"}]","Advancing Fault Diagnosis in Aircraft Landing Gear - An Innovative Two-Tier Machine Learning Approach - with Intelligent Sensor Data Management | PDF",1785813067,38,{"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},"advancing-fault-diagnosis-in-aircraft-landing-gear-an-innovative-two-tier-machine-learning-approach-with-intelligent-sensor-data-management","",{"@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/advancing-fault-diagnosis-in-aircraft-landing-gear-an-innovative-two-tier-machine-learning-approach-with-intelligent-sensor-data-management/122819/",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","2026-08-04",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 landing gear health monitoring critical for aviation?","Question",{"text":75,"@type":76},"Landing gear health monitoring directly supports passenger safety and economic efficiency. The document cites a significant share of aviation accidents linked to landing gear failure.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the proposed two-tier method address in fault diagnosis?",{"text":80,"@type":76},"It addresses sensor data anomalies that can mislead machine learning models, causing false alarms or missed/incorrect fault predictions.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the two-tier model improve diagnostic accuracy?",{"text":84,"@type":76},"The primary tier focuses on fault classification, while the secondary tier corrects sensor irregularities using redundant sensor inputs, improving accuracy from 95.88% to 98.76% after imputation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]