[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123304-en":3,"doc-seo-123304-105":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":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},123304,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools - Abstract","Ball screws are critical drive elements in machine tool feed axes, yet their failures cause substantial downtime and manufacturing costs that erode company competitiveness. When physical model parameters are unavailable, data-based monitoring infers ball screw condition from sensor signals. Method selection depends strongly on whether fault data exist, since most literature relies on artificially created fault patterns. This work develops machine-learning monitoring for machine tool fleets, comparing AutoML supervised anomaly detection and semi-supervised unified outlier scoring when fault labels are missing.","The International Journal of Advanced Manufacturing Technology (2023) 127:1143–1164  \n[https://doi.org/10.1007/s00170-023-1](https://doi.org/10.1007/s00170-023-1)1524-9  \nApplication of machine learning for fleet‑based condition monitoring of ball screw drives in machine tools  \nBerend Denkena1 · Marc‑André Dittrich1 · Hendrik Noske1 · Dirk Lange2 · Carolin Benjamins3 · Marius Lindauer3  \nReceived: 1 February 2023 / Accepted: 3 May 2023 / Published online: 23 May 2023 © The Author(s) 2023  \nAbstract  \nBall screws are frequently used as drive elements in the feed axes of machine tools. The failure of ball screw drives is associated with high downtimes and costs for manufacturing companies, which harm competitiveness. Data-based monitoring approaches derive the ball screw condition based on sensor data in cases where no knowledge is available to derive a physical model-based approach. An essential criterion for selecting the condition assessment method is the availability of fault data. In the literature, fault patterns are often artificially created in an experimental test bench scenario. This paper presents ballscrew drive monitoring approaches for machine tool fleets based on machine learning. First, the potentials of automated machine learning for supervised anomaly detection are investigated. It is shown that the AutoML tool Auto-Sklearn achievesa higher monitoring quality compared to literature approaches. However, fault data are often not available. Therefore, unified outlier scores are applied in a semi-supervised anomaly detection mode. The unified outlier score approach outperforms threshold-based approaches commonly used in industry. The considered data set originates from a machine tool fleet used in series production in the automotive industry collected over 8 months. Within the observation period, multiple ball screw failures are observed so that sensor data about the transient phases between normal and fault conditions is available.  \nKeywords Condition monitoring · Machine learning · Ball screw · Failure  \n1 Introduction  \n1.1 Need for condition monitoring of ball screw drives in machine tools  \nMachine tool feed drives are used for high-precision positioning of the milling tool and workpiece. Ball screw drives are suitable for this task due to their high-efficiency level [1, 2]. Ball screws also exhibit low heating and length variation and high positioning accuracy [3] . Ball screws also have a low failure frequency. However, in case of failure, high downtime follows, reducing machine tools’ technical availability. A total of 38% of the downtimes of feed axes are  \n* Hendrik Noske[noske@ifw.uni-hannover.de](noske@ifw.uni-hannover.de)  \n1 Institute of Production Engineering and Machine Tools, Ander Universität 2, 30823 Garbsen, Germany  \n2 Marposs Monitoring Solutions GmbH, Buchenring 40, 21272 Egestorf, Germany  \n3 Institute of Artificial Intelligence, Appelstraße 9a,  \n30167 Hannover, Germany  \ncaused by ball screws and feed axes, accounting for nearly 40% of the leading causes of machine tool failure [3] . A ball screw drive consists of multiple components, including a raceway, ball screw, screw nut, drive motor, support bearings, and the table. The ball screw is subjected to preloading to increase rigidity [4] . Various types of ball screw damage exist. In the case of sudden early damage, running instability occurs due to damage sustained by the deflection elements resulting in defects of balls and the raceways. Gradual late damage occurs in ball screws used for longer than the intended operating time. In this case, pitting is created in the raceway and ball surfaces, leading to running irregularities. Another type of damage is the insidious loss of preload. Over time, the ball diameter decreases, reducing the preload and, thus, the stiffness properties of the drive. The stiffness variations increase the chatter tendency of the axis, and thereby surface tolerances of workpieces can no longer be maintained [5]","cbCaioLGYISjotqx","https://ap.wps.com/l/cbCaioLGYISjotqx","pdf",2828943,1,22,"English","en",105,"# 1 Introduction\n## 1.1 Need for condition monitoring of ball screw drives in machine tools\n## 1.2 Our contribution","[{\"question\":\"Why is condition monitoring important for ball screw drives in machine tools?\",\"answer\":\"Ball screw failures lead to high downtime and significant replacement costs, reducing machine tool availability and affecting manufacturing competitiveness.\"},{\"question\":\"How do data-based monitoring methods work when no physical model is available?\",\"answer\":\"They infer ball screw condition directly from sensor data, learning system behavior from past measurements rather than using parameterized physical models.\"},{\"question\":\"What strategy is used when fault data are not available?\",\"answer\":\"The approach applies semi-supervised anomaly detection using unified outlier scores, which are compared against commonly used threshold-based methods.\"}]","Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools - Abstract | PDF",1785815840,55,{"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},"application-of-machine-learning-for-fleet-based-condition-monitoring-of-ball-screw-drives-in-machine-tools-abstract","",{"@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/application-of-machine-learning-for-fleet-based-condition-monitoring-of-ball-screw-drives-in-machine-tools-abstract/123304/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is condition monitoring important for ball screw drives in machine tools?","Question",{"text":75,"@type":76},"Ball screw failures lead to high downtime and significant replacement costs, reducing machine tool availability and affecting manufacturing competitiveness.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do data-based monitoring methods work when no physical model is available?",{"text":80,"@type":76},"They infer ball screw condition directly from sensor data, learning system behavior from past measurements rather than using parameterized physical models.",{"name":82,"@type":73,"acceptedAnswer":83},"What strategy is used when fault data are not available?",{"text":84,"@type":76},"The approach applies semi-supervised anomaly detection using unified outlier scores, which are compared against commonly used threshold-based methods.","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"]