[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118304-en":3,"doc-seo-118304-105":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},118304,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Based Prognostics of On-Board Electromechanical Actuators - paper and results","This paper proposes a novel machine learning-based prognostic method for on-board electromechanical actuators. The approach targets key limitations of model-based prognostics that depend on computationally expensive optimization and extensive fault modeling. Machine learning is used to map system signal characteristics directly to parameters for fault simulation. Two tests assess fault prediction and detection accuracy, robustness, error rates, and computational costs. The outcome supports a viable real-time solution for fault detection and characterization in industrial applications.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nMachine Learning Based Prognostics of On-Board Electromechanical Actuators  \nOriginal  \nMachine Learning Based Prognostics of On-Board Electromechanical Actuators / Minisci, Edmondo; Dalla Vedova, Matteo; Alimhillaj, Parid; Baldo, Leonardo; Maggiore, Paolo (LECTURE NOTES ON MULTIDISCIPLINARY INDUSTRIAL ENGINEERING) . -In: Lecture Notes on Multidisciplinary Industrial EngineeringELETTRONICO. -Berlino : Springer Nature, 2024. -ISBN 9783031489327. -pp. 148-159 [10 . 1007/978-3-031-48933-4_ 15]  \nAvailability:  \nThis version is available at: 11583/2991506 since: 2024-08-09T11:25:02Z  \nPublisher:  \nSpringer Nature  \nPublished  \nDOI:10 . 1007/978-3-031-48933-4_ 15  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nSpringer postprint/Author's Accepted Manuscript (book chapters)  \nThis is a post-peer-review, pre-copyedit version of a book chapter published in Lecture Notes on Multidisciplinary Industrial Engineering. The final authenticated version is available online at: [http://dx.doi.org/10.1007/978-3-031-48933-](http://dx.doi.org/10.1007/978-3-031-48933-)[ ](http://dx.doi.org/10.1007/978-3-031-48933-)[4_15](4_15)  \n(Article begins on next page)  \nMachine Learning based Prognostics of On-Board Electromechanical Actuators  \nEdmondo MINISCI1[0000-0001-9951-8528] , Matteo D.L. DALLA VEDOVA2[0000-0002-3124- 2198], Parid ALIMHILLAJ3[], Leonardo BALDO2[0000-0001-5073-4166], and Paolo  \nMAGGIORE2[0000-0003-0218-0730]  \n1 University of Strathclyde, Glasgow G1 1XJ, United Kingdom  \n2 Politecnico di Torino, Turin 10129, Italy  \n3 Polytechnic University of Tirana, Tirana 1000, Albania [edmondo.minisci@strath.ac.uk](edmondo.minisci@strath.ac.uk), matteo.dallavedova@polito.it,  \n[parid.alimhillaj@fim.edu.al](parid.alimhillaj@fim.edu.al), leonardo.baldo@polito.it, paolo.maggiore@polito.it  \nAbstract. This paper presents a novel machine learning-based prognostic approach for on-board electromechanical actuators. The study is centered around overcoming the limitations of model-based prognostic frameworks that rely on expensive optimization processes. Machine learning techniques were employed to map system signal characteristics directly into parameters related to fault simulation. A first test, utilizing only five of eight implemented fault types, demonstrates a highly promising potential of artificial neural networks to predict and detect faults with minimal error. A second test expand the investigation to include all fault types and provides an analysis of the model's robustness, error rates, and computational costs. The practical outcome of the work is a viable real-time solution for fault detection and characterization in electromechanical actuators, highlighting the efficiency and effectiveness of machine learning techniques for industrial applications.  \nKeywords: Machine Learning, Prognostics, Electro-Mechanical Actuators, Reliability, Fault Implementation, Failure Analysis  \n1 Introduction  \nElectromechanical actuators (EMAs) play a pivotal role in modern technological systems, converting electrical energy into mechanical energy and performing precise motion control tasks. As crucial components in industries such as manufacturing, automotive, and robotics, they underpin the smooth functioning of a myriad of systems. Their importance is even further underscored in the aerospace and aeronautical sectors where EMAs are vital components in aircraft systems including flight control, landing gears, and engine control, with the contemporary trend of transitioning from hydraulic and pneumatic systems to \"more electric aircraft\" [ 1] . This transition is driven by the advantages of EMAs, such as weight reduction, simplified maintenance, and overall efficiency improvement[2,3] .  \nThese wide-ranging applications accentuate the necessity for early fault detection and system r","cbCaih3oe0x0h02j","https://ap.wps.com/l/cbCaih3oe0x0h02j","pdf",728474,1,13,"English","en",105,"# Introduction\n## Electromechanical actuators and application needs\n## Limitations of model-based diagnostic and prognostic methods\n## Shift to data-driven machine learning approaches\n## Gap and study objective","[{\"question\":\"What problem does the paper address in prognostics for on-board electromechanical actuators?\",\"answer\":\"It addresses the inefficiency of model-based prognostic frameworks that rely on expensive optimization and heavy computational processes for real-time fault detection.\"},{\"question\":\"How does the proposed method use machine learning?\",\"answer\":\"Machine learning techniques map actuator signal characteristics directly to parameters related to fault simulation, reducing reliance on complex model-based optimization.\"},{\"question\":\"What do the two tests evaluate?\",\"answer\":\"The first test uses five of eight fault types to demonstrate fault prediction/detection with minimal error, while the second includes all fault types to analyze robustness, error rates, and computational costs.\"}]","Machine Learning Based Prognostics of On-Board Electromechanical Actuators - 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