[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124451-en":3,"doc-seo-124451-105":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},124451,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Load torque estimation for cable failure detection in cable-driven parallel robots - a machine learning approach","This paper presents a cable failure detection method for cable-driven parallel robots (CDPRs) with arbitrary architecture using estimates of motor load torques and supervised machine learning. Open-loop load torque observers are designed for each motor by exploiting the actuator dynamic model in no-load conditions, enabling a robust failure signature when the coupled load drops to zero under a broken cable. Detection relies on trained classifiers using a hybrid dataset combining numerically generated cases from a multibody digital twin and real nonfailing measurements. The method supports rigid and flexible cables, slackness effects, and both fully actuated and redundantly actuated CDPR configurations.","Multibody System Dynamics  \n[https://doi.org/10.1007/s1](https://doi.org/10.1007/s1)1044-024-10023-3  \nRESEARCH  \nLoad torque estimation for cable failure detection in  \ncable-driven parallel robots: a machine learning approach Jason Bettega1 · Giulio Piva1 · Dario Richiedei1 · Alberto Trevisani1  \nReceived: 31 October 2023 / Accepted: 9 August 2024 © The Author(s) 2024  \nAbstract  \nThis paper proposes a method for cable failure detection in cable-driven parallel robots (CDPRs) with arbitrary architecture, which is based on the estimates of the motor load torques, together with machine learning algorithms. By just exploiting the dynamic model of each actuator in the conditions of no load, an open-loop load torque observer is designed for each motor to estimate the presence of a load coupled through a cable. Since such a load instantaneously goes to zero for the motor with a broken cable, a simple but effective and robust signature of failure can be inferred to provide reliable detection even in the case of various model mismatches. Additionally, the load torque observer is not computationally demanding since just motor measurements are required, thus avoiding any direct measurement (and a dynamic model as well) on the end-effector. The detection of a failure is made through supervised classification algorithms based on artificial intelligence. The training of the machine learning algorithm is based on a “hybrid” approach: the dataset includes several failure cases, which are numerically generated through a system digital twin developed through the multibody system theory, together with measurements ofthe real system in nonfailing conditions. Different classification algorithms are considered, together with different sets of input variables to be fed to the classifier. Four numerical examples are proposed by showing the method capability in handling both fully actuated and redundantly actuated CDPRs under cable failure, both rigid and flexible cables, and also evaluating the response in the presence of cable slackness.  \nKeywords Cable-driven parallel robots · Multibody systems · Cable failure detection · Machine learning · Supervised classifier · Load torque observer  \n􀀂 D. Richiedei  \n[dario.richiedei@unipd.it](dario.richiedei@unipd.it)  \nJ. Bettega  \n[jason.bettega@unipd.it](jason.bettega@unipd.it)  \nG. Piva  \n[giulio.piva@phd.unipd.it](giulio.piva@phd.unipd.it)  \nA. Trevisani  \n[alberto.trevisani@unipd.it](alberto.trevisani@unipd.it)  \n1 Department of Management and Engineering, University of Padova, Stradella S. Nicola 3, 36100 Vicenza, Italy  \n1 Introduction  \n1.1 Motivations and state ofthe art  \nCable-driven parallel robots (CDPRs) are gaining increasing attention in the field of multibody system dynamics due to their benefits in terms of large workspaces and payloads, and small energy requirements. On the other hand, the cable can just pull, and positive tensions must be ensured, thus setting several challenges in the design [1, 2], modeling [3, 4], motion planning [5], and control [6, 7] . Besides these difficulties, CDPRs can be affected by cable failures [8], which severely compromise the robot operation and cause hazardous situations resulting in damages to the system itself and the surroundings.  \nThe literature on CDPRs has recently proposed several approaches for recovery after failure, to drive the end-effector in a safe position that should belong to the feasible workspace of the CDPR with the broken cable (see, e.g., [9, 10]) .  \nIn contrast, the issue of identifying cable failure is barely discussed in the literature, and just one work addresses this issue, to the best ofthe authors’ knowledge. A failure identification process, together with a proper recovery strategy, is proposed in [11] for a planar reconfigurable CDPR; in particular, the pose estimation of the end-effector is combined with the failure detection through the exploitation of an interactive multiple model algorithm, which relies only on the information ","cbCaipETmCaqOj9A","https://ap.wps.com/l/cbCaipETmCaqOj9A","pdf",4881433,1,29,"English","en",105,"# Abstract\n# Introduction\n## Motivations and state of the art\n## Paper contributions","[{\"question\":\"How does the method detect cable failure in cable-driven parallel robots?\",\"answer\":\"It estimates motor load torques using an open-loop load torque observer, exploiting the fact that the coupled load instantaneously becomes zero for a motor when its cable is broken.\"},{\"question\":\"What is the role of the dataset in training the supervised classifier?\",\"answer\":\"Training uses a hybrid approach: failure cases are numerically generated through a system digital twin based on multibody theory, and combined with measurements from the real system under nonfailing conditions.\"},{\"question\":\"Why is the proposed load torque observer computationally efficient?\",\"answer\":\"It only requires motor measurements, avoiding direct end-effector measurement and also avoiding reliance on an end-effector dynamic model.\"}]","Load torque estimation for cable failure detection in cable-driven parallel robots - a machine learning approach | PDF",1785822361,73,{"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},"load-torque-estimation-for-cable-failure-detection-in-cable-driven-parallel-robots-a-machine-learning-approach","",{"@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/load-torque-estimation-for-cable-failure-detection-in-cable-driven-parallel-robots-a-machine-learning-approach/124451/",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},"How does the method detect cable failure in cable-driven parallel robots?","Question",{"text":75,"@type":76},"It estimates motor load torques using an open-loop load torque observer, exploiting the fact that the coupled load instantaneously becomes zero for a motor when its cable is broken.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the role of the dataset in training the supervised classifier?",{"text":80,"@type":76},"Training uses a hybrid approach: failure cases are numerically generated through a system digital twin based on multibody theory, and combined with measurements from the real system under nonfailing conditions.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is the proposed load torque observer computationally efficient?",{"text":84,"@type":76},"It only requires motor measurements, avoiding direct end-effector measurement and also avoiding reliance on an end-effector dynamic model.","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"]