[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128737-en":3,"doc-seo-128737-105":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},128737,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A Machine Learning Dataset of Artificial Inner Ring Damage on Cylindrical Roller Bearings Measured Under Varying Cross-Influences","Practical machine learning for rotating machinery is sensitive to covariate shifts and hidden dependencies, which can reduce robustness and degrade prediction quality when data distributions change. This work provides a curated dataset of undamaged and artificially damaged cylindrical roller bearings to support studies of domain and distribution shifts. Multiple covariates—mounting position, load, and rotational speed—are defined across optimized levels for group-based cross-validation, enabling users to hold out selected groups during training, validation, and testing. Algorithms can then be evaluated for robustness and generalization under realistic distribution shift conditions.","Data Descriptor  \nA Machine Learning Dataset of Artificial Inner Ring Damage on Cylindrical Roller Bearings Measured Under Varying  \nCross-Influences  \nChristopher Schnur 1,2, *, Payman Goodarzi 1, Yannick Robin 1, Julian Schauer 1,2 and Andreas Schütze 1,2  \nAcademic Editor: Juraj Gregá ˇn  \nReceived: 6 April 2025  \nRevised: 11 May 2025  \nAccepted: 13 May 2025  \nPublished: 16 May 2025  \nCitation: Schnur, C.; Goodarzi, P.; Robin, Y.; Schauer, J.; Schütze, A. A Machine Learning Dataset of Artificial Inner Ring Damage on Cylindrical Roller Bearings Measured Under Varying Cross-Influences. Data 2025, 10, 77. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)data10050077  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Lab for Measurement Technology, Saarland University, 66123 Saarbrücken, Germany  \n2 Centre for Mechatronics and Automation Technology gGmbH, 66121 Saarbrücken, Germany  \n* Correspondence: info@lmt.uni-saarland.de; Tel.: +49-681-302-4664  \nAbstract: In practical machine learning (ML) applications, covariate shifts and dependencies can significantly impact model robustness and prediction quality, leading to performance degradation under distribution shifts. In industrial settings, it is crucial to account for covariates during the design of experiments to ensure reliable generalization. The presented dataset of undamaged and artificially damaged cylindrical roller bearings is designed to address the lack of data resources for targeting domain and distribution shiftsin this field. The dataset considers multiple key covariates, including mounting position, load, and rotational speed. Each covariate consists of multiple levels optimized for groupbased cross-validation. This allows the user to exclude specific groups in the training to validate and test the algorithm. Using this approach, algorithms can be evaluated for their robustness and the effect on the model caused by distribution shifts, allowing their generalization capabilities to be studied under realistic conditions.  \nDataset: Published on Zenodo. DOI: 10.5281/zenodo.11108503 (MATLAB), 10.5281/zenodo.11108503 (CSV/Python)  \nDataset License: Creative Commons Attribution 4.0 International (CC-BY)  \nKeywords: machine learning; robust learning; domain shift; bearing dataset  \n1. Introduction  \nRoller bearings are widely used rotating machine elements that reduce friction and carry loads. Although bearings are considered robust and have a long service life, incorrect usage can lead to unexpected bearing failure and, eventually, machine failure. Typical bearing failures are, e.g., wear, corrosion, or fracture and cracking [1] . In particular, pitting corrosion, which forms small hole-like corrosion pits in the metal, can cause severe damage to the bearing and result in failure [2] . To investigate these damage characteristics using machine learning (ML), multiple datasets are publicly available, e.g.,:  \n• NASA bearing dataset [3]: The dataset contains acceleration measurements with four bearings that are stressed with a constant load until they reach their wear limit.  \n• Paderborn University Bearing Dataset [4]: The dataset contains acceleration, rotational speed, load, and torque measurements of 26 damaged (artificial and real) and six undamaged bearings in four scenarios.  \n• Case Western Reserve University Bearing Dataset [5]: The dataset contains measurements of an accelerometer for artificially damaged bearings with different damage sizes and loads.  \nThe datasets mentioned above incorporate covariates to a limited extent, such as load or rotational speed. In real-world scenarios, several additional covariates may occur simultaneously and i","cbCaibMy9RWtbfoK","https://ap.wps.com/l/cbCaibMy9RWtbfoK","pdf",25116088,1,12,"English","en",105,"# Introduction\n## Background and motivation\n## Related public bearing datasets\n## Dataset design and objectives\n# Methods\n## Bearing configuration and artificial inner-ring damage","[{\"question\":\"What problem does the dataset address in machine learning for bearings?\",\"answer\":\"It targets the lack of data resources for analyzing domain and distribution shifts, where covariates and their interactions can reduce model robustness under changing conditions.\"},{\"question\":\"Which covariates are included and why are they important?\",\"answer\":\"The dataset includes mounting position, load, and rotational speed. These covariates are key drivers for distribution changes, so controlling them helps assess robustness and generalization.\"},{\"question\":\"How does the dataset support evaluation of robustness under distribution shifts?\",\"answer\":\"It uses multiple covariate levels and group-based cross-validation, allowing specific groups to be excluded from training and used for validation and testing to measure effects of distribution shifts.\"}]","A Machine Learning Dataset of Artificial Inner Ring Damage on Cylindrical Roller Bearings Measured Under Varying Cross-Influences | PDF",1786002964,30,{"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},"a-machine-learning-dataset-of-artificial-inner-ring-damage-on-cylindrical-roller-bearings-measured-under-varying-cross-influences","",{"@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/a-machine-learning-dataset-of-artificial-inner-ring-damage-on-cylindrical-roller-bearings-measured-under-varying-cross-influences/128737/",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","2026-08-23","2026-08-06",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},"What problem does the dataset address in machine learning for bearings?","Question",{"text":76,"@type":77},"It targets the lack of data resources for analyzing domain and distribution shifts, where covariates and their interactions can reduce model robustness under changing conditions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which covariates are included and why are they important?",{"text":81,"@type":77},"The dataset includes mounting position, load, and rotational speed. These covariates are key drivers for distribution changes, so controlling them helps assess robustness and generalization.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the dataset support evaluation of robustness under distribution shifts?",{"text":85,"@type":77},"It uses multiple covariate levels and group-based cross-validation, allowing specific groups to be excluded from training and used for validation and testing to measure effects of distribution shifts.","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},[94,98,102,106,111,116,121,123,128,131,135],{"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":29,"slug":122},"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":107,"slug":138},19,"General","general"]