[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118771-en":3,"doc-seo-118771-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118771,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine Learning for the Control and Monitoring of Electric Machine Drives - Advances and Trends","Systematically reviews existing literature on using machine learning techniques for the control and monitoring of electric machine drives, focusing on data-driven approaches that can deliver automated high-performance control and diagnostic capabilities. Highlights how rapid progress in learning algorithms and specialized embedded hardware enables practical deployment, while outlining an industry-oriented roadmap for running ML algorithms on embedded system-on-chip FPGA devices. Emphasizes embedded constraints and real-time update-rate requirements as key implementation drivers.","Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends  \nShen Zhang, Oliver Wallscheid, and Mario Porrmann  \narXiv :2110 .05403v2 [ ee ss . SY] 18 Feb 2023  \nAbstract—This review paper systematically summarizes the existing literature on utilizing machine learning (ML) techniques for the control and monitoring of electric machine drives. It is anticipated that with the rapid progress in learning algorithms and specialized embedded hardware platforms, machine learning-based data-driven approaches will become standard tools for the automated high-performance control and monitoring of electric drives. Additionally, this paper also provides some outlook toward promoting its widespread application in the industry with a focus on deploying ML algorithms onto embedded system-on-chip (SoC) field-programmable gate array (FPGA) devices.  \nIndex Terms—Machine learning; electric machine drives; deep learning; reinforcement learning; embedded systems; FPGA.  \nI. INTRODUCTION  \nThe motor control community is well-informed on the boom of machine learning (ML) after the modern back-propagation paper was first published in 1986 [1], which is evident by the work that appeared three years later on training a neural network offline to mimic the behavior of hysteresis current controllers in a three-phase PWM inverter [2] . This work is later followed by a series of pioneering efforts in the early 1990s on general voltage-fed AC machines [3], [4], induction machines [5]–[15], DC machines [16], [17], synchronous machines [18], and switched reluctance machines [19] . In addition to the broad interest in applying ML to motor drive control, such technologies, especially concerning classification or regression techniques, have also found their presence in the condition monitoring and fault diagnosis on various types of electric machines [20]–[27] .  \nAround that time, the frontier of power electronics gradually advanced with the advent of ML models such as neural networks, which have emerged as the most important area for complex system identification, control, and estimation in power electronics and motor drives [28] . However, it was also concluded that “in spite of the technology advancement, currently, industrial applications of neural networks in power electronics appear to be very few” [29] .  \nWhile ML applications always targeted the fastest available hardware platforms, especially focusing on (massively)  \nShen Zhang was with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. He is now with Joby Aviation, San Carlos, CA 94070, USA (e-mail: [shenzhang@gatech.edu](shenzhang@gatech.edu)).  \nOliver Wallscheid is with the Department of Automatic Control, Paderborn University, 33098 Paderborn, Germany (e-mail: oliver.wallscheid@unipaderborn.de) .  \nMario Porrmann is with the Institute of Computer Science, Osnabr¨ueck University, Wachsbleiche 27, 49090 Osnabr¨uck, Germany (email: [mario.porrmann@uni-osnabrueck.de](mario.porrmann@uni-osnabrueck.de)).  \nFig. 1. A vision of future electric machine drives powered by machine learning.  \nparallel architectures, many existing ML implementations in electric machine drives were based on slow and sequentially executed digital signal processors (DSP) prior to the deep learning era, although in some cases multiple DSPs were also used to increase the execution speed. Embedded platforms such as field-programmable gate arrays (FPGA) that excel at parallel processing, were not matured technologies at the time and had limited use.  \nIt is worth noting that hardware limitations are still the main bottleneck for ML applications in electric machine driveseven today. This remains a major problem particularly in the industrial world due to the high-frequency update rates 1 of ML algorithms required for motor drive online applications in combination with cost-oriented, computationally-constrained embedded hardware. This hardware cons","cbCaiteM8Q1JYGqe","https://ap.wps.com/l/cbCaiteM8Q1JYGqe","pdf",2484434,1,26,"English","en",105,"# Introduction\n## ML-driven control and monitoring in motor drives\n## Condition monitoring and fault diagnosis\n## Hardware platforms and deployment outlook","[{\"question\":\"这篇综述重点研究什么内容？\",\"answer\":\"该综述系统梳理了机器学习用于电机驱动控制与监测的方法与现有研究，并讨论数据驱动方案如何走向自动化高性能控制与监测。\"},{\"question\":\"文中提到机器学习在电机驱动中的应用有哪些方向？\",\"answer\":\"除控制相关技术外，还覆盖了基于分类或回归的状态监测与故障诊断。\"},{\"question\":\"为什么嵌入式硬件（尤其是FPGA）会成为部署重点？\",\"answer\":\"由于电机驱动在线应用需要很高的更新频率，并且对计算能力和成本有约束，因此文中强调将ML算法部署到嵌入式SoC与FPGA以提升可行性。\"}]","Machine Learning for the Control and Monitoring of Electric Machine Drives - Advances and Trends | PDF",1785720151,66,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-for-the-control-and-monitoring-of-electric-machine-drives-advances-and-trends","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-for-the-control-and-monitoring-of-electric-machine-drives-advances-and-trends/118771/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"这篇综述重点研究什么内容？","Question",{"text":76,"@type":77},"该综述系统梳理了机器学习用于电机驱动控制与监测的方法与现有研究，并讨论数据驱动方案如何走向自动化高性能控制与监测。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"文中提到机器学习在电机驱动中的应用有哪些方向？",{"text":81,"@type":77},"除控制相关技术外，还覆盖了基于分类或回归的状态监测与故障诊断。",{"name":83,"@type":74,"acceptedAnswer":84},"为什么嵌入式硬件（尤其是FPGA）会成为部署重点？",{"text":85,"@type":77},"由于电机驱动在线应用需要很高的更新频率，并且对计算能力和成本有约束，因此文中强调将ML算法部署到嵌入式SoC与FPGA以提升可行性。","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]