{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T05:18:50Z","timestamp":1780723130282,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Xinjiang Uygur Autonomous Region Key Research and Development Programs","award":["2022B02038"],"award-info":[{"award-number":["2022B02038"]}]},{"name":"Xinjiang Uygur Autonomous Region Key Research and Development Programs","award":["2022B03031"],"award-info":[{"award-number":["2022B03031"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The diagnosis of bearing faults is a crucial aspect of ensuring the optimal functioning of mechanical equipment. However, in practice, the use of small samples and variable operating conditions may result in suboptimal generalization performance, reduced accuracy, and overfitting for these methods. To address this challenge, this study proposes a bearing fault diagnosis method based on a symmetric two-stream convolutional neural network (CNN). The method employs hybrid signal processing techniques to address the issue of limited data. The method employs a symmetric parallel convolutional neural network (CNN) for the analysis of bearing data. Initially, the data are transformed into time\u2013frequency maps through the utilization of the short-time Fourier transform (STFT) and the simultaneous compressed wavelet transform (SCWT). Subsequently, two sets of one-dimensional vectors are generated by reconstructing the high-resolution features of the faulty samples using a symmetric parallel convolutional neural network (CNN). Feature splicing and fusion are then performed to generate bearing fault diagnosis information and assist fault classification. The experimental results demonstrate that the proposed mixed-signal processing method is effective on small-sample datasets, and verify the feasibility and generality of the symmetric parallel CNN-support vector machine (SVM) model for bearing fault diagnosis under small-sample conditions.<\/jats:p>","DOI":"10.3390\/sym16091178","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T04:15:01Z","timestamp":1725855301000},"page":"1178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Research on Small Sample Rolling Bearing Fault Diagnosis Method Based on Mixed Signal Processing Technology"],"prefix":"10.3390","volume":"16","author":[{"given":"Peibo","family":"Yu","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baobao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Software, Xinjiang University, Urumqi 830091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhui","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihang","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"042047","DOI":"10.1088\/1742-6596\/1885\/4\/042047","article-title":"Exploration of the safety management and maintenance of machinery manufacturing and processing equipment","volume":"1885","author":"Wu","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"012067","DOI":"10.1088\/1742-6596\/1007\/1\/012067","article-title":"Identification of bearing failure using signal vibrations","volume":"1007","author":"Yani","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1007\/s00339-023-07155-2","article-title":"Investigation on dynamic stability of Timoshenko beam using axial parametric excitation","volume":"129","author":"Firouzi","year":"2023","journal-title":"Appl. Phys. A"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"200","DOI":"10.4028\/www.scientific.net\/AMM.616.200","article-title":"Analysis of the damage causes of high speed bearing failure","volume":"616","author":"Simkulet","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Asad, B., Kudelina, K., Vaimann, T., and Kallaste, A. (2022). The bearing faults detection methods for electrical machines\u2014The state of the art. Energies, 16.","DOI":"10.3390\/en16010296"},{"key":"ref_6","first-page":"155598","article-title":"Machine learning based bearing fault diagnosis using the case western reserve university data: A review","volume":"9","author":"Zhang","year":"2021","journal-title":"EEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hu, L., Wang, L., Chen, Y., Hu, N., and Jiang, Y. (2022). Bearing fault diagnosis using piecewise aggregate approximation and complete ensemble empirical mode decomposition with adaptive noise. Sensors, 22.","DOI":"10.3390\/s22176599"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Barcelos, A.S., and Cardoso, A.J.M. (2021). Current-based bearing fault diagnosis using deep learning algorithms. Energies, 14.","DOI":"10.3390\/en14092509"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e218","DOI":"10.1002\/mp.13764","article-title":"Computer-aided diagnosis in the era of deep learning","volume":"47","author":"Chan","year":"2020","journal-title":"Med. Phys."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1109\/TIE.2020.2982085","article-title":"Domain knowledge-based deep-broad learning framework for fault diagnosis","volume":"68","author":"Feng","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.neucom.2021.02.078","article-title":"An optimized adaptive PReLU-DBN for rolling element bearing fault diagnosis","volume":"445","author":"Niu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Qin, Y., and Shi, X. (2022). Fault diagnosis method for rolling bearings based on two-channel CNN under unbalanced datasets. Appl. Sci., 12.","DOI":"10.3390\/app12178474"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"110500","DOI":"10.1016\/j.measurement.2021.110500","article-title":"Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method","volume":"189","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"6298","DOI":"10.1109\/TIE.2021.3086707","article-title":"A multisource dense adaptation adversarial network for fault diagnosis of machinery","volume":"69","author":"Huang","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3894","DOI":"10.1109\/TII.2021.3112504","article-title":"Task-sequencing meta learning for intelligent few-shot fault diagnosis with limited data","volume":"18","author":"Hu","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, L., Liu, Y., and Gao, L. (2021). Wavelet-prototypical network based on fusion of time and frequency domain for fault diagnosis. Sensors, 21.","DOI":"10.3390\/s21041483"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Altaf, M., Akram, T., Khan, M.A., Iqbal, M., Ch, M.M.I., and Hsu, C.-H. (2022). A new statistical features based approach for bearing fault diagnosis using vibration signals. Sensors, 22.","DOI":"10.3390\/s22052012"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"084007","DOI":"10.1088\/1361-6501\/abe5e3","article-title":"Data augmentation for rolling bearing fault diagnosis using an enhanced few-shot Wasserstein auto-encoder with meta-learning","volume":"32","author":"Pei","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_21","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":"2019","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"79510","DOI":"10.1109\/ACCESS.2019.2923417","article-title":"Data segmentation and augmentation methods based on raw data using deep neural networks approach for rotating machinery fault diagnosis","volume":"7","author":"Meng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","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":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Han, S., Niu, P., Luo, S., Li, Y., Zhen, D., Feng, G., and Sun, S. (2023). A novel deep convolutional neural network combining global feature extraction and detailed feature extraction for bearing compound fault diagnosis. Sensors, 23.","DOI":"10.3390\/s23198060"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9598","DOI":"10.1109\/JSEN.2022.3163658","article-title":"Data augmentation for intelligent mechanical fault diagnosis based on local shared multiple-generator GAN","volume":"22","author":"Guo","year":"2022","journal-title":"IEEE Sensors J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"125112","DOI":"10.1088\/1361-6501\/ac856d","article-title":"Lightweight model-based two-step fine-tuning for fault diagnosis with limited data","volume":"33","author":"Tang","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"22711","DOI":"10.1109\/ACCESS.2021.3055826","article-title":"A convolutional neural network approach to the classification of engineering models","volume":"9","author":"Manda","year":"2021","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"012107","DOI":"10.1088\/1742-6596\/1820\/1\/012107","article-title":"Review on Fault Diagnosis on the Rolling Bearing","volume":"1820","author":"Zhang","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bapir, A., and Aydin, I. (2022, January 23\u201325). A comparative analysis of 1D convolutional neural networks for bearing fault diagnosis. Proceedings of the 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand.","DOI":"10.1109\/DASA54658.2022.9765229"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"012017","DOI":"10.1088\/1742-6596\/2511\/1\/012017","article-title":"Experimental monitoring of vibrations and the problem of amplitude quantification","volume":"2511","author":"Schneider","year":"2023","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jain, S., Salman, H., Khaddaj, A., Wong, E., Park, S.M., and M\u0105dry, A. (2023, January 17\u201324). A data-based perspective on transfer learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00352"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7828","DOI":"10.1109\/JIOT.2023.3319630","article-title":"Brainstorming Generative Adversarial Networks (BGANs): Towards Multi-Agent Generative Models with Distributed Datasets","volume":"11","author":"Ferdowsi","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1093\/comjnl\/bxac033","article-title":"Improving the performance of feature selection methods with low-sample-size data","volume":"66","author":"Zheng","year":"2022","journal-title":"Comput. J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Alvarez, R., Borbor, E., and Grijalva, F. (2019, January 13\u201315). Comparison of methods for signal analysis in the time-frequency domain. Proceedings of the 2019 IEEE Fourth Ecuador Technical Chapters Meeting (ETCM), Guayaquil, Ecuador.","DOI":"10.1109\/ETCM48019.2019.9014860"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1109\/19.963221","article-title":"Joint time-frequency resolution of signal analysis using Gabor transform","volume":"50","author":"Zielinski","year":"2001","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Guo, J., Wang, X., Zhai, C., Niu, J., and Lu, S. (2019, January 19\u201321). Fault diagnosis of wind turbine bearing using synchrosqueezing wavelet transform and order analysis. Proceedings of the 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi\u2019an, China.","DOI":"10.1109\/ICIEA.2019.8833879"},{"key":"ref_38","first-page":"139","article-title":"Diesel engine fault diagnosis based on an improved convolutional neural network","volume":"41","author":"Zhang","year":"2022","journal-title":"J. Vib. Shock"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4501952","DOI":"10.1155\/2018\/4501952","article-title":"Convolutional Recurrent Neural Network for Fault Diagnosis of High-Speed Train Bogie","volume":"2018","author":"Liang","year":"2018","journal-title":"Complexity"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hu, P., Zhao, C., Huang, J., and Song, T. (2023). Intelligent and Small Samples Gear Fault Detection Based on Wavelet Analysis and Improved CNN. Processes, 11.","DOI":"10.3390\/pr11102969"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ymssp.2015.04.021","article-title":"Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study","volume":"64\u201365","author":"Smith","year":"2015","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"109327","DOI":"10.1016\/j.dib.2023.109327","article-title":"University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets","volume":"49","author":"Sehri","year":"2023","journal-title":"Data Brief"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/9\/1178\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:51:53Z","timestamp":1760111513000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/9\/1178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,9]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["sym16091178"],"URL":"https:\/\/doi.org\/10.3390\/sym16091178","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,9]]}}}