{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T20:30:57Z","timestamp":1774125057400,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T00:00:00Z","timestamp":1658275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["ALLRP 555220 \u2013 20"],"award-info":[{"award-number":["ALLRP 555220 \u2013 20"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Bearings are vital components of rotating machines that are prone to unexpected faults. Therefore, bearing fault diagnosis and condition monitoring are essential for reducing operational costs and downtime in numerous industries. In various production conditions, bearings can be operated under a range of loads and speeds, which causes different vibration patterns associated with each fault type. Normal data are ample as systems usually work in desired conditions. On the other hand, fault data are rare, and in many conditions, there are no data recorded for the fault classes. Accessing fault data is crucial for developing data-driven fault diagnosis tools that can improve both the performance and safety of operations. To this end, a novel algorithm based on conditional generative adversarial networks (CGANs) was introduced. Trained on the normal and fault data on actual fault conditions, this algorithm generates fault data from normal data of target conditions. The proposed method was validated on a real-world bearing dataset, and fault data were generated for different conditions. Several state-of-the-art classifiers and visualization models were implemented to evaluate the quality of the synthesized data. The results demonstrate the efficacy of the proposed algorithm.<\/jats:p>","DOI":"10.3390\/s22145413","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T03:34:40Z","timestamp":1658374480000},"page":"5413","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Synthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN"],"prefix":"10.3390","volume":"22","author":[{"given":"Maryam","family":"Ahang","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8013-8613","authenticated-orcid":false,"given":"Masoud","family":"Jalayer","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ardeshir","family":"Shojaeinasab","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oluwaseyi","family":"Ogunfowora","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5982-255X","authenticated-orcid":false,"given":"Todd","family":"Charter","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Homayoun","family":"Najjaran","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada"},{"name":"Department of Mechanical Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/0022-460X(85)90390-6","article-title":"The vibration produced by multiple point defects in a rolling element bearing","volume":"98","author":"McFadden","year":"1985","journal-title":"J. Sound Vib."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"29857","DOI":"10.1109\/ACCESS.2020.2972859","article-title":"Deep learning algorithms for bearing fault diagnostics\u2014A comprehensive review","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.jmsy.2022.01.004","article-title":"Intelligent manufacturing execution systems: A systematic review","volume":"62","author":"Shojaeinasab","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ymssp.2018.02.016","article-title":"Artificial intelligence for fault diagnosis of rotating machinery: A review","volume":"108","author":"Liu","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","article-title":"A new convolutional neural network-based data-driven fault diagnosis method","volume":"65","author":"Wen","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/MCSE.2018.110113254","article-title":"Fault state recognition of rolling bearing based fully convolutional network","volume":"21","author":"Zhang","year":"2018","journal-title":"Comput. Sci. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.ymssp.2017.03.034","article-title":"A novel deep autoencoder feature learning method for rotating machinery fault diagnosis","volume":"95","author":"Shao","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, F., Dun, B., Deng, G., Li, H., and Han, Q. (2018, January 14\u201317). A deep neural network based on kernel function and auto-encoder for bearing fault diagnosis. Proceedings of the 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, USA.","DOI":"10.1109\/I2MTC.2018.8409574"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1109\/TIE.2017.2745473","article-title":"Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network","volume":"65","author":"Shao","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107589","DOI":"10.1016\/j.measurement.2020.107589","article-title":"A novelty detection scheme for rolling bearing based on multiscale fuzzy distribution entropy and hybrid kernel convex hull approximation","volume":"156","author":"Zhao","year":"2020","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1177\/1475921718788299","article-title":"Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis","volume":"18","author":"Meruane","year":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9515","DOI":"10.1109\/ACCESS.2018.2890693","article-title":"Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: A comparative study","volume":"7","author":"Mao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.compind.2019.01.001","article-title":"Generative adversarial networks for data augmentation in machine fault diagnosis","volume":"106","author":"Shao","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Berghout, T., and Benbouzid, M. (2022). A Systematic Guide for Predicting Remaining Useful Life with Machine Learning. Electronics, 11.","DOI":"10.3390\/electronics11071125"},{"key":"ref_15","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_16","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":"J. Intell. Manuf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2604191","DOI":"10.1155\/2020\/2604191","article-title":"Wasserstein generative adversarial network and convolutional neural network (WG-CNN) for bearing fault diagnosis","volume":"2020","author":"Yin","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106587","DOI":"10.1016\/j.ymssp.2019.106587","article-title":"Applications of machine learning to machine fault diagnosis: A review and roadmap","volume":"138","author":"Lei","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103380","DOI":"10.1016\/j.compind.2020.103380","article-title":"A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines","volume":"125","author":"Schwendemann","year":"2021","journal-title":"Comput. Ind."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Karamizadeh, S., Abdullah, S.M., Halimi, M., Shayan, J., and javad Rajabi, M. (2014, January 2\u20134). Advantage and drawback of support vector machine functionality. Proceedings of the 2014 International Conference on Computer, Communications, and Control Technology (I4CT), Langkawi, Malaysia.","DOI":"10.1109\/I4CT.2014.6914146"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.neucom.2020.04.045","article-title":"A systematic review of deep transfer learning for machinery fault diagnosis","volume":"407","author":"Li","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isatra.2021.02.042","article-title":"Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions","volume":"119","author":"Zhang","year":"2022","journal-title":"ISA Trans."},{"key":"ref_24","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pan, T., Chen, J., Zhang, T., Liu, S., He, S., and Lv, H. (2021). Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives. ISA Trans., in press.","DOI":"10.1016\/j.isatra.2021.11.040"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"70111","DOI":"10.1109\/ACCESS.2020.2986356","article-title":"Data fusion generative adversarial network for multi-class imbalanced fault diagnosis of rotating machinery","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","first-page":"1","article-title":"SASLN: Signals augmented self-taught learning networks for mechanical fault diagnosis under small sample condition","volume":"70","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"109467","DOI":"10.1016\/j.measurement.2021.109467","article-title":"Enhanced generative adversarial network for extremely imbalanced fault diagnosis of rotating machine","volume":"180","author":"Wang","year":"2021","journal-title":"Measurement"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106577","DOI":"10.1016\/j.asoc.2020.106577","article-title":"Hybrid attribute conditional adversarial denoising autoencoder for zero-shot classification of mechanical intelligent fault diagnosis","volume":"95","author":"Lv","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_30","first-page":"1","article-title":"A novel method for imbalanced fault diagnosis of rotating machinery based on generative adversarial networks","volume":"70","author":"Li","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106333","DOI":"10.1016\/j.asoc.2020.106333","article-title":"Imbalanced sample fault diagnosis of rotating machinery using conditional variational auto-encoder generative adversarial network","volume":"92","author":"Wang","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_32","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"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.neucom.2018.05.024","article-title":"An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition","volume":"310","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.2987413","article-title":"Fault diagnosis of bearing in wind turbine gearbox under actual operating conditions driven by limited data with noise labels","volume":"70","author":"Huang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3127634","article-title":"A novel multitask adversarial network via redundant lifting for multicomponent intelligent fault detection under sharp speed variation","volume":"70","author":"Shi","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6282","DOI":"10.1109\/TII.2020.3030967","article-title":"Deep feature generating network: A new method for intelligent fault detection of mechanical systems under class imbalance","volume":"17","author":"Pan","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104837","DOI":"10.1016\/j.knosys.2019.07.008","article-title":"Deep learning fault diagnosis method based on global optimization GAN for unbalanced data","volume":"187","author":"Zhou","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_38","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.neucom.2018.10.109","article-title":"Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty","volume":"396","author":"Gao","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"10130","DOI":"10.1109\/TIE.2020.3028821","article-title":"A small sample focused intelligent fault diagnosis scheme of machines via multimodules learning with gradient penalized generative adversarial networks","volume":"68","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xie, Y., and Zhang, T. (2018, January 25\u201327). Imbalanced learning for fault diagnosis problem of rotating machinery based on generative adversarial networks. Proceedings of the 2018 37th Chinese Control Conference (CCC), Wuhan, China.","DOI":"10.23919\/ChiCC.2018.8483334"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wu, C., and Zeng, Z. (2021). A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0246905"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"115025","DOI":"10.1088\/1361-6501\/ac18d2","article-title":"An efficient method for imbalanced fault diagnosis of rotating machinery","volume":"32","author":"Yang","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_44","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":"Mech. Syst. Signal Process."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107768","DOI":"10.1016\/j.measurement.2020.107768","article-title":"Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial nets and convolutional neural network","volume":"159","author":"Liang","year":"2020","journal-title":"Measurement"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/5.58337","article-title":"Backpropagation through time: What it does and how to do it","volume":"78","author":"Werbos","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_47","unstructured":"Staudemeyer, R.C., and Morris, E.R. (2019). Understanding LSTM\u2013a tutorial into long short-term memory recurrent neural networks. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Shao, X., and Kim, C.S. (2022). Unsupervised Domain Adaptive 1D-CNN for Fault Diagnosis of Bearing. Sensors, 22.","DOI":"10.3390\/s22114156"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Tang, H., Gao, S., Wang, L., Li, X., Li, B., and Pang, S. (2021). A novel intelligent fault diagnosis method for rolling bearings based on Wasserstein generative adversarial network and Convolutional Neural Network under Unbalanced Dataset. Sensors, 21.","DOI":"10.3390\/s21206754"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kahr, M., Kov\u00e1cs, G., Loinig, M., and Br\u00fcckl, H. (2022). Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data. Sensors, 22.","DOI":"10.3390\/s22072490"},{"key":"ref_51","unstructured":"O\u2019Shea, K., and Nash, R. (2015). An introduction to convolutional neural networks. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"103378","DOI":"10.1016\/j.compind.2020.103378","article-title":"Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms","volume":"125","author":"Jalayer","year":"2021","journal-title":"Comput. Ind."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, C., and Wand, M. (2016). Precomputed real-time texture synthesis with markovian generative adversarial networks. European Conference on Computer Vision, Proceedings of the 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016, Springer.","DOI":"10.1007\/978-3-319-46487-9_43"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"27869","DOI":"10.3390\/s151127869","article-title":"A novel characteristic frequency bands extraction method for automatic bearing fault diagnosis based on Hilbert Huang transform","volume":"15","author":"Yu","year":"2015","journal-title":"Sensors"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Jalayer, M., Kaboli, A., Orsenigo, C., and Vercellis, C. (2022). Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery. Machines, 10.","DOI":"10.3390\/machines10040237"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5413\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:54:34Z","timestamp":1760140474000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5413"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,20]]},"references-count":56,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145413"],"URL":"https:\/\/doi.org\/10.3390\/s22145413","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,20]]}}}