{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:39:11Z","timestamp":1770273551873,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,12]],"date-time":"2021-10-12T00:00:00Z","timestamp":1633996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52075401"],"award-info":[{"award-number":["52075401"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rolling bearings are widely used in industrial manufacturing, and ensuring their stable and effective fault detection is a core requirement in the manufacturing process. However, it is a great challenge to achieve a highly accurate rolling bearing fault diagnosis because of the severe imbalance and distribution differences in fault data due to weak early fault features and interference from environmental noise. An intelligent fault diagnosis strategy for rolling bearings based on grayscale image transformation, a generative adversative network, and a convolutional neural network was proposed to solve this problem. First, the original vibration signal is converted into a grayscale image. Then more training samples are generated using GANs to solve severe imbalance and distribution differences in fault data. Finally, the rolling bearing condition detection and fault identification are carried out by using SECNN. The availability of the method is substantiated by experiments on datasets with different data imbalance ratios. In addition, the superiority of this diagnosis strategy is verified by comparing it with other mainstream intelligent diagnosis techniques. The experimental result demonstrates that this strategy can reach more than 99.6% recognition accuracy even under substantial environmental noise interference or changing working conditions and has good stability in the presence of a severe imbalance in fault data.<\/jats:p>","DOI":"10.3390\/s21206754","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T06:38:41Z","timestamp":1634107121000},"page":"6754","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Wasserstein Generative Adversarial Network and Convolutional Neural Network under Unbalanced Dataset"],"prefix":"10.3390","volume":"21","author":[{"given":"Hongtao","family":"Tang","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Digital Manufacturing, School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Shengbo","family":"Gao","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Digital Manufacturing, School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Digital Manufacturing, School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5796-3479","authenticated-orcid":false,"given":"Xixing","family":"Li","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Modern Manufacturing and Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China"}]},{"given":"Bing","family":"Li","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Digital Manufacturing, School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Shibao","family":"Pang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Digital Manufacturing, School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108469","DOI":"10.1016\/j.measurement.2020.108469","article-title":"Fault feature extraction for rolling bearings based on parameter-adaptive variational mode decomposition and multi-point optimal minimum entropy deconvolution","volume":"173","author":"Zhou","year":"2021","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.ymssp.2019.02.056","article-title":"Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis","volume":"126","author":"Li","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shenfield, A., and Howarth, M. (2020). A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults. Sensors, 20.","DOI":"10.3390\/s20185112"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.isatra.2019.09.020","article-title":"Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on CRH trains","volume":"99","author":"Wu","year":"2020","journal-title":"ISA Trans."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5905","DOI":"10.1109\/TSMC.2019.2956806","article-title":"Fault-Tolerant Control for Dynamic Positioning Vessel With Thruster Faults Based on the Neural Modified Extended State Observer","volume":"51","author":"Yu","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern.-Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1109\/TSMC.2017.2757264","article-title":"A Descriptor System Approach for Estimation of Incipient Faults With Application to High-Speed Railway Traction Devices","volume":"49","author":"Wu","year":"2019","journal-title":"IEEE Trans. Syst. Man Cybern.-Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"115368","DOI":"10.1109\/ACCESS.2019.2936243","article-title":"Improved Deep Transfer Auto-Encoder for Fault Diagnosis of Gearbox Under Variable Working Conditions With Small Training Samples","volume":"7","author":"He","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.measurement.2018.06.016","article-title":"A new l0-norm embedded MED method for roller element bearing fault diagnosis at early stage of damage","volume":"127","author":"Jiang","year":"2018","journal-title":"Measurement"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105313","DOI":"10.1016\/j.knosys.2019.105313","article-title":"Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples","volume":"191","author":"He","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wan, L., Chen, Y., Li, H., and Li, C. (2020). Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network. Sensors, 20.","DOI":"10.3390\/s20061693"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.eswa.2016.07.039","article-title":"A new support vector data description method for machinery fault diagnosis with unbalanced datasets","volume":"64","author":"Duan","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.measurement.2014.09.045","article-title":"Fault diagnosis approach for rotating machinery based on dynamic model and computational intelligence","volume":"59","author":"Zhang","year":"2015","journal-title":"Measurement"},{"key":"ref_13","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., and Bengio, Y. (2014). Generative Adversarial Nets. arXiv."},{"key":"ref_14","unstructured":"Shi, H., Dong, J., Wang, W., Qian, Y., and Zhang, X. (2017, January 28\u201329). SSGAN: Secure Steganography Based on Generative Adversarial Networks. Proceedings of the 18th Pacific-Rim Conference on Multimedia, Harbin, China."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1109\/TMI.2019.2946059","article-title":"Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning","volume":"39","author":"Gong","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Feng, H., Guo, J., Xu, H., and Ge, S.S. (2021). SharpGAN: Dynamic Scene Deblurring Method for Smart Ship Based on Receptive Field Block and Generative Adversarial Networks. Sensors, 21.","DOI":"10.3390\/s21113641"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1109\/LGRS.2018.2806945","article-title":"IMG2DSM: Height Simulation From Single Imagery Using Conditional Generative Adversarial Net","volume":"15","author":"Ghamisi","year":"2018","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_18","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_19","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017). Improved Training of Wasserstein GANs. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"174317","DOI":"10.1109\/ACCESS.2020.3026084","article-title":"An Ensemble Wasserstein Generative Adversarial Network Method for Road Extraction from High Resolution Remote Sensing Images in Rural Areas","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","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":"Inf. Sci."},{"key":"ref_22","first-page":"1","article-title":"Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty","volume":"2020","author":"Han","year":"2020","journal-title":"Shock. Vib."},{"key":"ref_23","first-page":"1","article-title":"A Current Signal-Based Adaptive Semisupervised Framework for Bearing Faults Diagnosis in Drivetrains","volume":"70","author":"Li","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, C., Xiong, D., Zhang, Q., and Liao, M. (2019). Parallel Connected Generative Adversarial Network with Quadratic Operation for SAR Image Generation and Application for Classification. Sensors, 19.","DOI":"10.3390\/s19040871"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.measurement.2018.01.036","article-title":"Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification","volume":"118","author":"Han","year":"2018","journal-title":"Measurement"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.compind.2019.02.001","article-title":"Deep convolutional neural network based planet bearing fault classification","volume":"107","author":"Zhao","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TIM.2017.2669947","article-title":"Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network","volume":"66","author":"Chen","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s40313-015-0173-7","article-title":"A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions","volume":"26","author":"Abed","year":"2015","journal-title":"J. Control Autom. Electr. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.ymssp.2017.09.026","article-title":"A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders","volume":"102","author":"Shao","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.cogsys.2018.03.002","article-title":"Rolling element bearing fault diagnosis using convolutional neural network and vibration image","volume":"53","author":"Hoang","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2446","DOI":"10.1109\/TII.2018.2864759","article-title":"Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning","volume":"15","author":"Shao","year":"2019","journal-title":"IIEEE Trans. Ind. Inform."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yao, Y., Zhang, S., Yang, S., and Gui, G. (2020). Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions. Sensors, 20.","DOI":"10.3390\/s20041233"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.jsv.2016.05.027","article-title":"Convolutional Neural Network Based Fault Detection for Rotating Machinery","volume":"377","author":"Janssens","year":"2016","journal-title":"J. Sound Vib."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.ymssp.2017.06.022","article-title":"A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load","volume":"100","author":"Zhang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"10278","DOI":"10.1109\/ACCESS.2018.2888842","article-title":"A Novel Deeper One-Dimensional CNN With Residual Learning for Fault Diagnosis of Wheelset Bearings in High-Speed Trains","volume":"7","author":"Peng","year":"2018","journal-title":"IEEE Access"},{"key":"ref_36","first-page":"124","article-title":"Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network","volume":"37","author":"Li","year":"2018","journal-title":"J. Vib. Shock."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1109\/TIM.2017.2674738","article-title":"Energy-Fluctuated Multiscale Feature Learning with Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis","volume":"66","author":"Ding","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., and Tang, X. (2017, January 21\u201326). Residual Attention Network for Image Classification. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Du, J., Cheng, K., Yu, Y., Wang, D., and Zhou, H. (2021). Panchromatic Image Super-Resolution Via Self Attention-Augmented Wasserstein Generative Adversarial Network. Sensors, 21.","DOI":"10.3390\/s21062158"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TMI.2018.2867261","article-title":"Recalibrating Fully Convolutional Networks with Spatial and Channel \u2018Squeeze & Excitation\u2019 Blocks","volume":"38","author":"Roy","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Feng, Y., Chen, J., Zhang, T., He, S., Xu, E., and Zhou, Z. (2021). Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis. ISA Trans.","DOI":"10.1016\/j.isatra.2021.03.013"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"655","DOI":"10.5545\/sv-jme.2010.162","article-title":"Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two-Dimension Domain","volume":"57","author":"Do","year":"2011","journal-title":"Stroj. Vestn.-J. Mech. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Long, J., Wang, H., Zha, D., Li, P., Xie, H., and Mao, L. (2017). Applications of fractional lower order S transform time frequency filtering algorithm to machine fault diagnosis. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0175202"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4659","DOI":"10.1109\/TIM.2019.2956613","article-title":"Intelligent Fault Diagnosis via Semi-Supervised Generative Adversarial Nets and Wavelet Transform","volume":"69","author":"Liang","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.aei.2017.02.005","article-title":"Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification","volume":"32","author":"Lu","year":"2017","journal-title":"Adv. Eng. Inform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.measurement.2016.07.054","article-title":"Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis","volume":"93","author":"Guo","year":"2016","journal-title":"Measurement"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yang, Z.-X., Wang, X.-B., and Zhong, J.-H. (2016). Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach. Energies, 9.","DOI":"10.3390\/en9060379"},{"key":"ref_50","first-page":"443","article-title":"An Improved Bearing Fault Diagnosis Method using One-Dimensional CNN and LSTM","volume":"64","author":"Pan","year":"2018","journal-title":"Stroj. Vestn.-J. Mech. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Zhou, R., and Dong, Z. (2019, January 27\u201330). Aero-Engine Faults Diagnosis Based on K-Means Improved Wasserstein GAN and Relevant Vector Machine. Proceedings of the 38th Chinese Control Conference, Guangzhou, China.","DOI":"10.23919\/ChiCC.2019.8865682"},{"key":"ref_52","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_53","first-page":"1","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/20\/6754\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:11:55Z","timestamp":1760166715000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/20\/6754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,12]]},"references-count":53,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21206754"],"URL":"https:\/\/doi.org\/10.3390\/s21206754","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,12]]}}}