{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T17:18:28Z","timestamp":1783790308723,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["LH2022E029"],"award-info":[{"award-number":["LH2022E029"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["LBH-Q21083"],"award-info":[{"award-number":["LBH-Q21083"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postdoctoral Research Foundation of Heilongjiang Province","award":["LH2022E029"],"award-info":[{"award-number":["LH2022E029"]}]},{"name":"Postdoctoral Research Foundation of Heilongjiang Province","award":["LBH-Q21083"],"award-info":[{"award-number":["LBH-Q21083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the worldwide carbon neutralization boom, low-speed heavy load bearings have been widely used in the field of wind power. Bearing failure generates impulses when the rolling element passes the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect failure signals. However, the high sampling rates of AE signals make it difficult to design and extract fault features; thus, deep neural network-based approaches have been proposed. In this paper, we proposed an improved RepVGG bearing fault diagnosis technique. The normalized and noise-reduced bearing signals were first converted into Mel frequency cepstrum coefficients (MFCCs) and then inputted into the model. In addition, the exponential moving average method was used to optimize the model and improve its accuracy. Data were extracted from the test bench and wind turbine main shaft bearing. Four damage classes were studied experimentally. The experimental results demonstrated that the improved RepVGG model could be employed for classifying low-speed heavy load bearing states by using MFCCs. Furthermore, the effectiveness of the proposed model was assessed by performing comparisons with existing models.<\/jats:p>","DOI":"10.3390\/s23073541","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T01:33:00Z","timestamp":1680053580000},"page":"3541","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Extreme-Low-Speed Heavy Load Bearing Fault Diagnosis by Using Improved RepVGG and Acoustic Emission Signals"],"prefix":"10.3390","volume":"23","author":[{"given":"Peng","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3361-8097","authenticated-orcid":false,"given":"Wenyu","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Tongtai Hengsheng Technology Co., Ltd., Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rogelj, J., Schaeffer, M., Meinshausen, M., Knutti, R., Alcamo, J., Riahi, K., and Hare, W. (2015). Zero emission targets as long-term global goals for climate protection. Environ. Res. Lett., 10.","DOI":"10.1088\/1748-9326\/10\/10\/105007"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, H., Chen, J., Han, G., and Cui, Q. (2022). Winding down the wind power curtailment in China: What made the difference?. Renew. Sustain. Energy Rev., 167.","DOI":"10.1016\/j.rser.2022.112725"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1080\/15376494.2018.1508795","article-title":"Design of a kinematic vibration energy harvester for a smart bearing with piezoelectric\/magnetic coupling","volume":"27","author":"Brusa","year":"2020","journal-title":"Mech. Adv. Mater. Struct."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Garcia-Calva, T., Morinigo-Sotelo, D., Fernandez-Cavero, V., and Romero-Troncoso, R. (2022). Early detection of faults in induction motors\u2014A review. Energies, 15.","DOI":"10.3390\/en15217855"},{"key":"ref_5","first-page":"800","article-title":"A review on acoustic emission analysis for bearing fault detection and classification","volume":"145","author":"Bharadwaj","year":"2019","journal-title":"Measurement"},{"key":"ref_6","first-page":"121","article-title":"Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: A review","volume":"69","author":"Ali","year":"2014","journal-title":"J. Teknol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wu, G., Yan, T., Yang, G., Chai, H., and Cao, C. (2022). A review on rolling bearing fault signal detection methods based on different sensors. Sensors, 22.","DOI":"10.3390\/s22218330"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kim, J.Y., and Kim, J.M. (2020). Bearing fault diagnosis using grad-CAM and acoustic emission signals. App. Sci., 10.","DOI":"10.3390\/app10062050"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1090\/S0025-5718-1965-0178586-1","article-title":"An algorithm for the machine calculation of complex Fourier series","volume":"19","author":"Cooley","year":"1965","journal-title":"Math. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kra\u015bny, M.J., and Bowen, C.R. (2021). A system for characterisation of piezoelectric materials and associated electronics for vibration powered energy harvesting devices. Measurement, 168.","DOI":"10.1016\/j.measurement.2020.108285"},{"key":"ref_11","first-page":"731","article-title":"Application of improved Hilbert envelope analysis for incipient fault diagnosis of rolling bearings","volume":"99","author":"Zhang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lu, Q., Shen, X., Wang, X., Li, M., Li, J., and Zhang, M. (2021). Fault diagnosis of rolling bearing based on improved VMD and KNN. Math. Probl. Eng., 2021.","DOI":"10.1155\/2021\/2530315"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6980","DOI":"10.1016\/j.eswa.2010.12.017","article-title":"SVM practical industrial application for mechanical faults diagnostic","volume":"38","author":"Baccarini","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3172","DOI":"10.1109\/JSEN.2019.2958787","article-title":"Interpretable convolutional neural network through layer-wise relevance propagation for machine fault diagnosis","volume":"20","author":"Grezmak","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, G., Deng, C., Wu, J., Chen, Z., and Xu, X. (2020). Rolling bearing fault diagnosis based on wavelet packet transform and convolutional neural network. Appl. Sci., 10.","DOI":"10.3390\/app10030770"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Di Maggio, L.G. (2022). Intelligent fault diagnosis of industrial bearings using transfer learning and CNNs pre-trained for audio classification. Sensors, 23.","DOI":"10.3390\/s23010211"},{"key":"ref_20","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","author":"Smith","year":"2015","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hendriks, J., Dumond, P., and Knox, D.A. (2022). Towards better benchmarking using the CWRU bearing fault dataset. Mech. Syst. Signal Process., 169.","DOI":"10.1016\/j.ymssp.2021.108732"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021). RepVGG: Making VGG-style ConvNets Great Again. arXiv.","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational mode decomposition","volume":"62","author":"Dragomiretskiy","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1080\/00031305.2014.917055","article-title":"Kurtosis as peakedness, 1905\u20132014. RIP","volume":"68","author":"Westfall","year":"2014","journal-title":"Am. Stat."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gu, X., Yang, S., Liu, Y., and Hao, R. (2016). Rolling element bearing faults diagnosis based on kurtogram and frequency domain correlated kurtosis. Meas. Sci. Technol., 27.","DOI":"10.1088\/0957-0233\/27\/12\/125019"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TASSP.1980.1163420","article-title":"Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences","volume":"28","author":"Davis","year":"1980","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cai, R., Wang, Q., Hou, Y., and Liu, H. (2021). Event monitoring of transformer discharge sounds based on voiceprint. J. Phys. Conf. Ser., 2078.","DOI":"10.1088\/1742-6596\/2078\/1\/012066"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/JCN.2018.000075","article-title":"Hidden Markov model based drone sound recognition using MFCC technique in practical noisy environments","volume":"20","author":"Shi","year":"2018","journal-title":"J. Commun. Netw."},{"key":"ref_29","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems 25 (NIPS 2012), Lake Tahoe, NV, USA."},{"key":"ref_30","unstructured":"LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and Jackel, L. (1989, January 27\u201330). Handwritten digit recognition with a back-propagation network. Proceedings of the Advances in Neural Information Processing Systems 25 (NIPS 2012), Denver, CO, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.ymssp.2018.05.050","article-title":"Deep learning and its applications to machine health monitoring","volume":"115","author":"Zhao","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., and Nandi, A.K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mech. Syst. Signal Process., 138.","DOI":"10.1016\/j.ymssp.2019.106587"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, C., Hang, B., Wang, S., and Chao, H.-C. (2019). An audio attention computational model based on information entropy of two channels and exponential moving average. Hum. Cent. Comput. Inf. Sci., 9.","DOI":"10.1186\/s13673-019-0166-9"},{"key":"ref_36","unstructured":"Bottou, L. (2012). Neural Networks: Tricks of the Trade, Springer."},{"key":"ref_37","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_39","unstructured":"Tan, M., and Le, Q.V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3541\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:05:04Z","timestamp":1760123104000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3541"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,28]]},"references-count":39,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23073541"],"URL":"https:\/\/doi.org\/10.3390\/s23073541","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,28]]}}}