{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T02:17:04Z","timestamp":1782181024787,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T00:00:00Z","timestamp":1584489600000},"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":["61702177"],"award-info":[{"award-number":["61702177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Platform Innovation Foundation of Hunan Provincial Education Department","award":["17K029"],"award-info":[{"award-number":["17K029"]}]},{"name":"Natural Science Foundation of Hunan Province, China","award":["2019JJ60048"],"award-info":[{"award-number":["2019JJ60048"]}]},{"name":"National Key Research and Development Project","award":["2018YFB1700204, 2018YFB1003401"],"award-info":[{"award-number":["2018YFB1700204, 2018YFB1003401"]}]},{"DOI":"10.13039\/501100019091","name":"Key Research and Development Project of Hunan Province","doi-asserted-by":"publisher","award":["2019GK2133"],"award-info":[{"award-number":["2019GK2133"]}],"id":[{"id":"10.13039\/501100019091","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional LeNet-5 network used in rolling-element bearing fault diagnosis, a rolling-element bearing fault diagnosis method using improved 2D LeNet-5 network is put forward. The following improvements to the traditional LeNet-5 network are made: the convolution and pooling layers are reasonably designed and the size and number of convolution kernels are carefully adjusted to improve fault classification capability; the batch normalization (BN) is adopted after each convolution layer to improve convergence speed; the dropout operation is performed after each full-connection layer except the last layer to enhance generalization ability. To further improve the efficiency and effectiveness of fault diagnosis, on the basis of improved 2D LeNet-5 network, an end-to-end rolling-element bearing fault diagnosis method based on the improved 1D LeNet-5 network is proposed, which can directly perform 1D convolution and pooling operations on raw vibration signals without any preprocessing. The results show that the improved 2D LeNet-5 network and improved 1D LeNet-5 network achieve a significant performance improvement than traditional LeNet-5 network, the improved 1D LeNet-5 network provides a higher fault diagnosis accuracy with a less training time in most cases, and the improved 2D LeNet-5 network performs better than improved 1D LeNet-5 network under small training samples and strong noise environment.<\/jats:p>","DOI":"10.3390\/s20061693","type":"journal-article","created":{"date-parts":[[2020,3,19]],"date-time":"2020-03-19T03:54:14Z","timestamp":1584590054000},"page":"1693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7236-3589","authenticated-orcid":false,"given":"Lanjun","family":"Wan","sequence":"first","affiliation":[{"name":"School of Computer, Hunan University of Technology, Zhuzhou 412007, China"},{"name":"Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiwei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer, Hunan University of Technology, Zhuzhou 412007, China"},{"name":"Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer, Hunan University of Technology, Zhuzhou 412007, China"},{"name":"Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changyun","family":"Li","sequence":"additional","affiliation":[{"name":"Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,18]]},"reference":[{"key":"ref_1","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. Sig. Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5618","DOI":"10.1109\/JSEN.2017.2727638","article-title":"Analysis of statistical time-domain features effectiveness in identification of bearing faults from vibration signal","volume":"17","author":"Nayana","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"055012","DOI":"10.1088\/1361-6501\/aaae99","article-title":"Extraction of repetitive transients with frequency domain multipoint kurtosis for bearing fault diagnosis","volume":"29","author":"Liao","year":"2018","journal-title":"Meas. Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isatra.2018.11.033","article-title":"Time\u2013frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets","volume":"87","author":"Zhang","year":"2019","journal-title":"ISA Trans."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"96","DOI":"10.3901\/CJME.2014.1103.166","article-title":"Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization","volume":"28","author":"Gao","year":"2015","journal-title":"Chin. Int. J. Mech. Eng. Educ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, Y., Xu, M., Huang, W., Zuo, M.J., and Liu, L. (2016, January 19\u201321). An improved EMD method for fault diagnosis of rolling bearing. Proceedings of the IEEE 2016 Prognostics and System Health Management Conference (PHM-Chengdu), Chengdu, China.","DOI":"10.1109\/PHM.2016.7819842"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.ymssp.2018.08.056","article-title":"Research on bearing fault feature extraction based on singular value decomposition and optimized frequency band entropy","volume":"118","author":"Li","year":"2019","journal-title":"Mech. Syst. Sig. Process."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, R., Huang, D., and Zhao, L. (2019, January 27\u201330). Fault diagnosis of rolling bearing based on EEMD information entropy and improved SVM. Proceedings of the IEEE Chinese Control Conference (CCC), Guangzhou, China.","DOI":"10.23919\/ChiCC.2019.8866102"},{"key":"ref_9","unstructured":"Appana, D.K., Islam, M.R., and Kim, J.M. (February, January 31). Reliable fault diagnosis of bearings using distance and density similarity on an enhanced k-NN. Proceedings of the Australasian Conference on Artificial Life and Computational Intelligence, Geelong, VIC, Australia."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shi, Z., Song, W., and Taheri, S. (2016). Improved LMD, permutation entropy and optimized K-means to fault diagnosis for roller bearings. Entropy, 18.","DOI":"10.3390\/e18030070"},{"key":"ref_11","first-page":"012092","article-title":"Bearing fault diagnosis based on BP neural network","volume":"Volume 208","author":"Lin","year":"2018","journal-title":"Proceedings of the IOP Conference Series: Earth and Environmental Science"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1109\/TIA.2017.2661250","article-title":"Deep learning based approach for bearing fault diagnosis","volume":"53","author":"He","year":"2017","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"740","DOI":"10.5545\/sv-jme.2016.3694","article-title":"Isomap and deep belief network-based machine health combined assessment model","volume":"62","author":"Yin","year":"2016","journal-title":"Stroj. Vestn.-J. Mech. E."},{"key":"ref_14","first-page":"6127479","article-title":"Rolling bearing fault diagnosis based on STFT-deep learning and sound signals","volume":"2016","author":"Liu","year":"2016","journal-title":"Shock Vibr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.isatra.2018.04.005","article-title":"Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders","volume":"77","author":"Liu","year":"2018","journal-title":"ISA Trans."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Hoang, D.T., and Kang, H.J. (2017, January 7\u201310). Convolutional neural network based bearing fault diagnosis. Proceedings of the International Conference on Intelligent Computing, Liverpool, UK.","DOI":"10.1007\/978-3-319-63312-1_9"},{"key":"ref_18","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. Inf."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"095005","DOI":"10.1088\/1361-6501\/aa6e22","article-title":"An adaptive deep convolutional neural network for rolling bearing fault diagnosis","volume":"28","author":"Fuan","year":"2017","journal-title":"Meas. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, S., Liu, G., Tang, X., Lu, J., and Hu, J. (2017). An ensemble deep convolutional neural network model with improved D-S evidence fusion for bearing fault diagnosis. Sensors, 17.","DOI":"10.3390\/s17081729"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, H., Yao, D., Yang, J., and Li, X. (2019). Lightweight convolutional neural network and its application in rolling bearing fault diagnosis under variable working conditions. Sensors, 19.","DOI":"10.3390\/s19224827"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Wen, L., Li, X., Li, X., and Gao, L. (2019, January 6\u20138). A new transfer learning based on VGG-19 network for fault diagnosis. Proceedings of the IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), Porto, Portugal.","DOI":"10.1109\/CSCWD.2019.8791884"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wen, L., Li, X., and Gao, L. (2019). A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Comput. Appl., 1\u201314.","DOI":"10.1007\/s00521-019-04097-w"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Eren, L. (2017). Bearing fault detection by one-dimensional convolutional neural networks. Math. Prob. Eng., 2017.","DOI":"10.1155\/2017\/8617315"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s11265-018-1378-3","article-title":"A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier","volume":"91","author":"Eren","year":"2019","journal-title":"J. Signal Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8136","DOI":"10.1109\/TIE.2018.2886789","article-title":"Fault detection and severity identification of ball bearings by online condition monitoring","volume":"66","author":"Abdeljaber","year":"2018","journal-title":"IEEE Trans. Ind. Electro"},{"key":"ref_29","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. Sig. Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ma, S., Cai, W., Liu, W., Shang, Z., and Liu, G. (2019). A lighted deep convolutional neural network based fault diagnosis of rotating machinery. Sensors, 19.","DOI":"10.3390\/s19102381"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Sarraf, S., and Tofighi, G. (2016, January 6\u20137). Deep learning-based pipeline to recognize Alzheimer\u2019s disease using fMRI data. Proceedings of the IEEE Future Technologies Conference (FTC), San Francisco, CA, USA.","DOI":"10.1101\/066910"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cao, J., Song, C., Peng, S., Xiao, F., and Song, S. (2019). Improved traffic sign detection and recognition algorithm for intelligent vehicles. Sensors, 19.","DOI":"10.3390\/s19184021"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, G., and Gong, J. (2019, January 3\u20135). Facial expression recognition based on improved LeNet-5 CNN. Proceedings of the IEEE 31st Chinese Control And Decision Conference (CCDC), Nanchang, China.","DOI":"10.1109\/CCDC.2019.8832535"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wei, G., Li, G., Zhao, J., and He, A. (2019). Development of a LeNet-5 gas identification CNN structure for electronic noses. Sensors, 19.","DOI":"10.3390\/s19010217"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1748302619873601","DOI":"10.1177\/1748302619873601","article-title":"Pedestrian detection based on improved LeNet-5 convolutional neural network","volume":"13","author":"Zhang","year":"2019","journal-title":"J. Algorithms Comput. Technol."},{"key":"ref_37","unstructured":"Case Western Reserve University Bearing Data Center (2019, July 08). Seeded Fault Test Data. Available online: https:\/\/csegroups.case.edu\/bearingdatacenter\/home."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/72.554195","article-title":"Face recognition: A convolutional neural-network approach","volume":"8","author":"Lawrence","year":"1997","journal-title":"IEEE Trans. Neural Networks"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"De Oliveira, M.A., Monteiro, A.V., and Vieira Filho, J. (2018). A new structural health monitoring strategy based on PZT sensors and convolutional neural network. Sensors, 18.","DOI":"10.20944\/preprints201808.0130.v1"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/S0963-8695(01)00044-5","article-title":"Rolling element bearing fault diagnosis using wavelet packets","volume":"35","author":"Nikolaou","year":"2002","journal-title":"NDT E Int."},{"key":"ref_41","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_42","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_43","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/6\/1693\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:07:42Z","timestamp":1760173662000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/6\/1693"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,18]]},"references-count":43,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20061693"],"URL":"https:\/\/doi.org\/10.3390\/s20061693","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,18]]}}}