{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T04:15:01Z","timestamp":1768796101637,"version":"3.49.0"},"reference-count":26,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":265,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100021130","name":"Bundesministerium f\u00fcr Wirtschaft und Klimaschutz","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100021130","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1016\/j.procs.2025.09.290","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T22:13:48Z","timestamp":1762467228000},"page":"1696-1705","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Comparison of Conditional and Non-Conditional Data Augmentation Approaches with Generative Adversarial Networks: A Case Study on Bearing Fault Diagnosis"],"prefix":"10.1016","volume":"270","author":[{"given":"Timo","family":"K\u00f6nig","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akash Mangaluru","family":"Ramananda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabian","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus","family":"Kley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcus","family":"Liebschner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.procs.2025.09.290_bib1","doi-asserted-by":"crossref","unstructured":"Ademujimi TT, Brundage MP, Prabhu VV. A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis. In: Advances in Production Management Systems. Cham: Springer International Publishing; 2017 pp. 407 https:\/\/doi.org\/10.1007\/978-3-319-66923-6_48","DOI":"10.1007\/978-3-319-66923-6_48"},{"key":"10.1016\/j.procs.2025.09.290_bib2","doi-asserted-by":"crossref","first-page":"1508","DOI":"10.1016\/j.procs.2024.09.602","article-title":"A LSTM-GAN Algorithm for Synthetic Data Generation of Time Series Data for Condition Monitoring","volume":"246","author":"K\u00f6nig","year":"2024","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.procs.2025.09.290_bib3","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.procs.2024.01.017","article-title":"Digitization Workflow for Data Mining in Production Technology applied to a Feed Axis of a CNC Milling Machine","volume":"232","author":"Drowatzky","year":"2024","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.procs.2025.09.290_bib4","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.procir.2014.08.006","article-title":"Challenges in Cost Analysis of Innovative Maintenance of Distributed High-value Assets","volume":"22","author":"Kirkwood","year":"2014","journal-title":"Procedia CIRP"},{"key":"10.1016\/j.procs.2025.09.290_bib5","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al. Generative Adversarial Networks 2014:1\u20139. https:\/\/doi.org\/10.48550\/arXiv.1406.2661"},{"key":"10.1016\/j.procs.2025.09.290_bib6","doi-asserted-by":"crossref","first-page":"108149","DOI":"10.1016\/j.comnet.2021.108149","article-title":"Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation","volume":"194","author":"Navidan","year":"2021","journal-title":"Computer Networks"},{"key":"10.1016\/j.procs.2025.09.290_bib7","unstructured":"Salehi P, Chalechale A, Taghizadeh M. Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments. 2020. https:\/\/doi.org\/10.48550\/arXiv.2005.13178; 2020."},{"key":"10.1016\/j.procs.2025.09.290_bib8","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A. Improved Training of Wasserstein GANs. 2017. https:\/\/doi.org\/10.48550\/arXiv.1704.00028"},{"key":"10.1016\/j.procs.2025.09.290_bib9","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1016\/j.procs.2023.10.363","article-title":"A Novel approach using WGAN-GP and Conditional WGAN-GP for Generating Artificial Thermal Images of Induction Motor Faults","volume":"225","author":"Hejazi","year":"2023","journal-title":"Procedia Computer Science"},{"issue":"2","key":"10.1016\/j.procs.2025.09.290_bib10","doi-asserted-by":"crossref","first-page":"74","DOI":"10.3390\/lubricants11020074","article-title":"Improvement of Generative Adversarial Network and Its Application in Bearing Fault Diagnosis: A Review","volume":"11","author":"Ruan","year":"2023","journal-title":"Lubricants"},{"issue":"20","key":"10.1016\/j.procs.2025.09.290_bib11","doi-asserted-by":"crossref","first-page":"19543","DOI":"10.1109\/JSEN.2022.3200691","article-title":"An Intelligent Fault Diagnosis Method of Small Sample Bearing Based on Improved Auxiliary Classification Generative Adversarial Network","volume":"22","author":"Meng","year":"2022","journal-title":"IEEE Sensors Journal"},{"key":"10.1016\/j.procs.2025.09.290_bib12","doi-asserted-by":"crossref","first-page":"110888","DOI":"10.1016\/j.measurement.2022.110888","article-title":"A conditional variational autoencoding generative adversarial networks with self-modulation for rolling bearing fault diagnosis","volume":"192","author":"Liu","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.procs.2025.09.290_bib13","unstructured":"Schaeffler Technologies. Condition Monitoring Praxis: Handbuch zur Schwingungs-Zustands\u00fcberwachung von Maschinen und Anlagen. 1st ed. Mainz am Rhein: Vereinigte Fachverlage; 2019."},{"issue":"2","key":"10.1016\/j.procs.2025.09.290_bib14","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/s10010-023-00668-5","article-title":"Enhanced damage classification accuracy on a transmission by extending existing datasets with generative adversarial networks","volume":"87","author":"K\u00f6nig","year":"2023","journal-title":"Forschung im Ingenieurwesen"},{"key":"10.1016\/j.procs.2025.09.290_bib15","first-page":"1777","article-title":"CE-CGAN: classification enhanced conditional generative adversarial networks for bearing fault diagnosis","volume":"21","author":"Qi","year":"2022","journal-title":"IET Conference Proceedings"},{"key":"10.1016\/j.procs.2025.09.290_bib16","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.procs.2022.09.118","article-title":"Generation of synthetic data with low-dimensional features for condition monitoring utilizing Generative Adversarial Networks","volume":"207","author":"Wagner","year":"2022","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.procs.2025.09.290_bib17","doi-asserted-by":"crossref","first-page":"107741","DOI":"10.1016\/j.measurement.2020.107741","article-title":"Data synthesis using dual discriminator conditional generative adversarial networks for imbalanced fault diagnosis of rolling bearings","volume":"158","author":"Zheng","year":"2020","journal-title":"Measurement"},{"issue":"2","key":"10.1016\/j.procs.2025.09.290_bib18","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s10845-020-01579-w","article-title":"A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis","volume":"32","author":"Luo","year":"2021","journal-title":"Journal of Intelligent Manufacturing"},{"key":"10.1016\/j.procs.2025.09.290_bib19","doi-asserted-by":"crossref","unstructured":"Ehrhart M, Resch B, Havas C, Niederseer D. A Conditional GAN for Generating Time Series Data for Stress Detection in Wearable Physiological Sensor Data. Sensors 2022;22(16). https:\/\/doi.org\/10.3390\/s22165969","DOI":"10.3390\/s22165969"},{"key":"10.1016\/j.procs.2025.09.290_bib20","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1016\/j.procs.2023.10.122","article-title":"A generative adversarial network-based data augmentation approach with transient vibration data","volume":"225","author":"Koenig","year":"2023","journal-title":"Procedia Computer Science"},{"issue":"5","key":"10.1016\/j.procs.2025.09.290_bib21","doi-asserted-by":"crossref","first-page":"336","DOI":"10.3390\/machines10050336","article-title":"Two-Stage Multi-Scale Fault Diagnosis Method for Rolling Bearings with Imbalanced Data","volume":"10","author":"Zheng","year":"2022","journal-title":"Machines"},{"issue":"14","key":"10.1016\/j.procs.2025.09.290_bib22","doi-asserted-by":"crossref","first-page":"7346","DOI":"10.3390\/app12147346","article-title":"A Novel Method for Fault Diagnosis of Bearings with Small and Imbalanced Data Based on Generative Adversarial Networks","volume":"12","author":"Tong","year":"2022","journal-title":"Applied Sciences"},{"key":"10.1016\/j.procs.2025.09.290_bib23","doi-asserted-by":"crossref","first-page":"108139","DOI":"10.1016\/j.ymssp.2021.108139","article-title":"Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis","volume":"163","author":"Liu","year":"2022","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.procs.2025.09.290_bib24","doi-asserted-by":"crossref","unstructured":"Randall RB. Vibration\u2010based Condition Monitoring: Industrial, Automotive and Aerospace Applications. Wiley. 2011. https:\/\/doi.org\/10.1002\/9780470977668","DOI":"10.1002\/9780470977668"},{"issue":"10","key":"10.1016\/j.procs.2025.09.290_bib25","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1515\/teme-2023-0001","article-title":"Synthetic data generation of vibration signals at different speed and load conditions of transmissions utilizing generative adversarial networks","volume":"90","author":"K\u00f6nig","year":"2023","journal-title":"Technisches Messen"},{"key":"10.1016\/j.procs.2025.09.290_bib26","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/j.ins.2019.10.014","article-title":"Conditional Wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification","volume":"512","author":"Zheng","year":"2020","journal-title":"Information Sciences"}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050925029631?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050925029631?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T15:11:30Z","timestamp":1768749090000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877050925029631"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":26,"alternative-id":["S1877050925029631"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2025.09.290","relation":{},"ISSN":["1877-0509"],"issn-type":[{"value":"1877-0509","type":"print"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Comparison of Conditional and Non-Conditional Data Augmentation Approaches with Generative Adversarial Networks: A Case Study on Bearing Fault Diagnosis","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2025.09.290","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}