{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:25:42Z","timestamp":1773415542269,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T00:00:00Z","timestamp":1719446400000},"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":["52201355"],"award-info":[{"award-number":["52201355"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52071090"],"award-info":[{"award-number":["52071090"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["060302132304"],"award-info":[{"award-number":["060302132304"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["060302132101"],"award-info":[{"award-number":["060302132101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022B01049"],"award-info":[{"award-number":["2022B01049"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023B01046"],"award-info":[{"award-number":["2023B01046"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Program for Scientific Research Start-Up Funds of Guangdong Ocean University","award":["52201355"],"award-info":[{"award-number":["52201355"]}]},{"name":"the Program for Scientific Research Start-Up Funds of Guangdong Ocean University","award":["52071090"],"award-info":[{"award-number":["52071090"]}]},{"name":"the Program for Scientific Research Start-Up Funds of Guangdong Ocean University","award":["060302132304"],"award-info":[{"award-number":["060302132304"]}]},{"name":"the Program for Scientific Research Start-Up Funds of Guangdong Ocean University","award":["060302132101"],"award-info":[{"award-number":["060302132101"]}]},{"name":"the Program for Scientific Research Start-Up Funds of Guangdong Ocean University","award":["2022B01049"],"award-info":[{"award-number":["2022B01049"]}]},{"name":"the Program for Scientific Research Start-Up Funds of Guangdong Ocean University","award":["2023B01046"],"award-info":[{"award-number":["2023B01046"]}]},{"name":"Zhanjiang Non-funded Science and Technology Research Project","award":["52201355"],"award-info":[{"award-number":["52201355"]}]},{"name":"Zhanjiang Non-funded Science and Technology Research Project","award":["52071090"],"award-info":[{"award-number":["52071090"]}]},{"name":"Zhanjiang Non-funded Science and Technology Research Project","award":["060302132304"],"award-info":[{"award-number":["060302132304"]}]},{"name":"Zhanjiang Non-funded Science and Technology Research Project","award":["060302132101"],"award-info":[{"award-number":["060302132101"]}]},{"name":"Zhanjiang Non-funded Science and Technology Research Project","award":["2022B01049"],"award-info":[{"award-number":["2022B01049"]}]},{"name":"Zhanjiang Non-funded Science and Technology Research Project","award":["2023B01046"],"award-info":[{"award-number":["2023B01046"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Regarding the difficulty of extracting the acquired fault signal features of bearings from a strong background noise vibration signal, coupled with the fact that one-dimensional (1D) signals provide limited fault information, an optimal time frequency fusion symmetric dot pattern (SDP) bearing fault feature enhancement and diagnosis method is proposed. Firstly, the vibration signals are transformed into two-dimensional (2D) features by the time frequency fusion algorithm SDP, which can multi-scale analyze the fluctuations of signals at minor scales, as well as enhance bearing fault features. Secondly, the bat algorithm is employed to optimize the SDP parameters adaptively. It can effectively improve the distinctions between various types of faults. Finally, the fault diagnosis model can be constructed by a deep convolutional neural network (DCNN). To validate the effectiveness of the proposed method, Case Western Reserve University\u2019s (CWRU) bearing fault dataset and bearing fault dataset laboratory experimental platform were used. The experimental results illustrate that the fault diagnosis accuracy of the proposed method is 100%, which proves the feasibility and effectiveness of the proposed method. By comparing with other 2D transformer methods, the experimental results illustrate that the proposed method achieves the highest accuracy in bearing fault diagnosis. It validated the superiority of the proposed methodology.<\/jats:p>","DOI":"10.3390\/s24134186","type":"journal-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T11:19:02Z","timestamp":1719487142000},"page":"4186","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Optimal Time Frequency Fusion Symmetric Dot Pattern Bearing Fault Feature Enhancement and Diagnosis"],"prefix":"10.3390","volume":"24","author":[{"given":"Guanlong","family":"Liang","sequence":"first","affiliation":[{"name":"Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang 524088, China"},{"name":"Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province, Zhanjiang 524088, China"},{"name":"Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Zhanjiang 524088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuewei","family":"Song","sequence":"additional","affiliation":[{"name":"Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang 524088, China"},{"name":"Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province, Zhanjiang 524088, China"},{"name":"Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Zhanjiang 524088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5637-486X","authenticated-orcid":false,"given":"Zhiqiang","family":"Liao","sequence":"additional","affiliation":[{"name":"Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang 524088, China"},{"name":"Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province, Zhanjiang 524088, China"},{"name":"Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Zhanjiang 524088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9928-5242","authenticated-orcid":false,"given":"Baozhu","family":"Jia","sequence":"additional","affiliation":[{"name":"Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang 524088, China"},{"name":"Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province, Zhanjiang 524088, China"},{"name":"Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Zhanjiang 524088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"A Bearing Fault Diagnosis Method Based on Concise Empirical Wavelet Transform","volume":"12","author":"Chaoyong","year":"2023","journal-title":"Int. J. Compr. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8326","DOI":"10.1109\/JSEN.2024.3356605","article-title":"A Semisupervised GCN Framework for Transfer Diagnosis Crossing Different Machines","volume":"24","author":"Song","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107582","DOI":"10.1016\/j.ymssp.2020.107582","article-title":"A Novel Fast Entrogram and Its Applications in Rolling Bearing Fault Diagnosis","volume":"154","author":"Zhang","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108402","DOI":"10.1016\/j.ymssp.2021.108402","article-title":"Measurement and Identification of the Nonlinear Dynamics of a Jointed Structure Using Full-Field Data; Part II\u2014Nonlinear System Identification","volume":"166","author":"Jin","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xue, H., Song, Z., Wu, M., Sun, N., and Wang, H. (2022). Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor. Sensors, 22.","DOI":"10.3390\/s22166316"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3511912","DOI":"10.1109\/TIM.2024.3370801","article-title":"Remaining Useful Life Prediction Method Based on the Spatiotemporal Graph and GCN Nested Parallel Route Model","volume":"73","author":"Song","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"117392","DOI":"10.1016\/j.oceaneng.2024.117392","article-title":"Early Bearing Fault Diagnosis for Imbalanced Data in Offshore Wind Turbine Using Improved Deep Learning Based on Scaled Minimum Unscented Kalman Filter","volume":"300","author":"Tang","year":"2024","journal-title":"Ocean Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1007\/s12206-024-0207-9","article-title":"An Improved Empirical Fourier Decomposition Method and Its Application in Fault Diagnosis of Rolling Bearing","volume":"38","author":"Pang","year":"2024","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1177\/14759217231178653","article-title":"An Enhanced Empirical Fourier Decomposition Method for Bearing Fault Diagnosis","volume":"23","author":"Zhu","year":"2024","journal-title":"Struct. Health Monit."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"590","DOI":"10.21595\/jve.2023.23673","article-title":"Rolling Bearing Fault Diagnosis Based on Variational Mode Decomposition and Weighted Multidimensional Feature Entropy Fusion","volume":"26","author":"Lei","year":"2024","journal-title":"J. Vibroeng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mao, M., Zeng, K., Tan, Z., Zeng, Z., Hu, Z., Chen, X., and Qin, C. (2023). Adaptive VMD\u2013K-SVD-Based Rolling Bearing Fault Signal Enhancement Study. Sensors, 23.","DOI":"10.3390\/s23208629"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6690966","DOI":"10.1155\/2021\/6690966","article-title":"Early Weak Fault Diagnosis of Rolling Bearing Based on Multilayer Reconstruction Filter","volume":"2021","author":"Li","year":"2021","journal-title":"Shock Vib."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107138","DOI":"10.1016\/j.engappai.2023.107138","article-title":"Feature Extraction of Multi-Sensors for Early Bearing Fault Diagnosis Using Deep Learning Based on Minimum Unscented Kalman Filter","volume":"127","author":"Tang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108401","DOI":"10.1016\/j.ymssp.2021.108402","article-title":"Measurement and identification of the nonlinear dynamics of a jointed structure using full-field data, Part I: Measurement of nonlinear dynamics","volume":"166","author":"Jin","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1007\/s00170-019-04726-7","article-title":"Rolling Element Bearing Fault Diagnosis for Rotating Machinery Using Vibration Spectrum Imaging and Convolutional Neural Networks","volume":"106","author":"Guersi","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, S., Cao, R., Xu, D., and Fan, Y. (2023). A Rolling Bearing Fault Diagnosis Method Based on the WOA-VMD and the GAT. Entropy, 25.","DOI":"10.3390\/e25060889"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1109\/JSEN.2022.3227099","article-title":"A Novel Fault Feature Selection and Diagnosis Method for Rotating Machinery with Symmetrized Dot Pattern Representation","volume":"23","author":"Tang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107293","DOI":"10.1016\/j.measurement.2019.107293","article-title":"Fault Diagnosis of Rolling Bearing Using Symmetrized Dot Pattern and Density-Based Clustering","volume":"152","author":"Li","year":"2020","journal-title":"Measurement"},{"key":"ref_19","first-page":"175","article-title":"Fault Diagnosis of Engines Based on SDP Image and Deep Convolutional Neural Network","volume":"43","author":"WANG","year":"2023","journal-title":"Noise Vib. Control"},{"key":"ref_20","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_21","first-page":"8026402","article-title":"Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model","volume":"2024","author":"Yuan","year":"2024","journal-title":"Shock Vib."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cui, W., Meng, G., Gou, T., Wang, A., Xiao, R., and Zhang, X. (2022). Intelligent Rolling Bearing Fault Diagnosis Method Using Symmetrized Dot Pattern Images and CBAM-DRN. Sensors, 22.","DOI":"10.3390\/s22249954"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"110178","DOI":"10.1016\/j.epsr.2024.110178","article-title":"Fault Classification and Location of a PMU-Equipped Active Distribution Network Using Deep Convolution Neural Network (CNN)","volume":"229","author":"Siddique","year":"2024","journal-title":"Electr. Power Syst. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112408","DOI":"10.1016\/j.measurement.2022.112408","article-title":"A Motor Bearing Fault Voiceprint Recognition Method Based on Mel-CNN Model","volume":"207","author":"Shan","year":"2023","journal-title":"Measurement"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"19963","DOI":"10.3934\/mbe.2023884","article-title":"Intelligent Fault Diagnosis Algorithm of Rolling Bearing Based on Optimization Algorithm Fusion Convolutional Neural Network","volume":"20","author":"Wang","year":"2023","journal-title":"Math. Biosci. Eng. MBE"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2201033","DOI":"10.1002\/ente.202201033","article-title":"Fault Diagnosis of Electric Submersible Pumps Using a Three-Stage Multiscale Feature Transformation Combined with CNN\u2013SVM","volume":"11","author":"Chen","year":"2023","journal-title":"Energy Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"972","DOI":"10.23919\/cje.2022.00.229","article-title":"Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN","volume":"32","author":"Chen","year":"2023","journal-title":"Chin. J. Electron."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1007\/s42417-023-00949-x","article-title":"Fault Diagnosis of Rolling Bearings Based on the Improved Symmetrized Dot Pattern Enhanced Convolutional Neural Networks","volume":"12","author":"Liu","year":"2024","journal-title":"J. Vib. Eng. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4186\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:06:26Z","timestamp":1760108786000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4186"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,27]]},"references-count":28,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24134186"],"URL":"https:\/\/doi.org\/10.3390\/s24134186","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,27]]}}}