{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:10:55Z","timestamp":1760130655736,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T00:00:00Z","timestamp":1692921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Open Foundation of the Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology","award":["2022ZDJS114","XQHZ202203","2021GCZX002"],"award-info":[{"award-number":["2022ZDJS114","XQHZ202203","2021GCZX002"]}]},{"name":"School-enterprise Cooperation Research Foundation of Shenzhen Technology University for Graduate Student","award":["2022ZDJS114","XQHZ202203","2021GCZX002"],"award-info":[{"award-number":["2022ZDJS114","XQHZ202203","2021GCZX002"]}]},{"name":"Guangdong University Engineering Technology Research Center for Precision Components of Intelligent Terminal of Transportation Tools","award":["2022ZDJS114","XQHZ202203","2021GCZX002"],"award-info":[{"award-number":["2022ZDJS114","XQHZ202203","2021GCZX002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the lack of fault data in the daily work of rotating machinery components, existing data-driven fault diagnosis procedures cannot accurately diagnose fault classes and are difficult to apply to most components. At the same time, the complex and variable working conditions of components pose a challenge to the feature extraction capability of the models. Therefore, a transferable pipeline is constructed to solve the fault diagnosis of multiple components in the presence of imbalanced data. Firstly, synchrosqueezed wavelet transforms (SWT) are improved to highlight the time-frequency feature of the signal and reduce the time-frequency differences between different signals. Secondly, we proposed a novel hierarchical window transformer model that obeys a dynamic seesaw (HWT-SS), which compensates for imbalanced samples while fully extracting key features of the samples. Finally, a transfer diagnosis between components provides a new approach to solving fault diagnosis with imbalanced data among multiple components. The comparison with the benchmark models in four datasets proves that the proposed model has the advantages of strong feature extraction capability and low influence from imbalanced data. The transfer tests between datasets and the visual interpretation of the model prove that the transfer diagnosis between components can further improve the diagnostic capability of the model for extremely imbalanced data.<\/jats:p>","DOI":"10.3390\/s23177431","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T06:10:22Z","timestamp":1693203022000},"page":"7431","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Component Transferable Transformer Pipeline Obeying Dynamic Seesaw for Rotating Machinery with Imbalanced Data"],"prefix":"10.3390","volume":"23","author":[{"given":"Binbin","family":"Xu","sequence":"first","affiliation":[{"name":"Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boquan","family":"Ma","sequence":"additional","affiliation":[{"name":"Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6733-7893","authenticated-orcid":false,"given":"Zheng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Chen","sequence":"additional","affiliation":[{"name":"Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobing","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology, Guangzhou 511370, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1007\/s42417-022-00498-9","article-title":"A review of data-driven machinery fault diagnosis using machine learning algorithms","volume":"10","author":"Cen","year":"2022","journal-title":"J. Vib. Eng. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, T., Chen, T., and Deng, W. (2023). Hyperspectral Image Classification Based on Fusing S3-PCA, 2D-SSA and Random Patch Network. Remote Sens., 15.","DOI":"10.3390\/rs15133402"},{"key":"ref_3","first-page":"1029","article-title":"Benchmarking Different Classification Techniques to Identify Depression Patterns In An Audio And Text Dataset","volume":"19","year":"2022","journal-title":"Webology"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Arellano-Espitia, F., Delgado-Prieto, M., Martinez-Viol, V., Saucedo-Dorantes, J.J., and Osornio-Rios, R.A. (2020). Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems. Sensors, 20.","DOI":"10.3390\/s20143949"},{"key":"ref_5","first-page":"110460","article-title":"Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review","volume":"189","author":"Yang","year":"2022","journal-title":"Measurment"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1177\/14759217211029201","article-title":"Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions","volume":"21","author":"Zhang","year":"2022","journal-title":"Struct. Health Monit. Int. J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Peng, C., Li, L.L., Chen, Q., Tang, Z.H., Gui, W.H., and He, J. (2021). A fault diagnosis method for rolling bearings based on parameter transfer learning under imbalance data sets. Energies, 14.","DOI":"10.3390\/en14040944"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.ins.2017.09.013","article-title":"Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets","volume":"422","author":"Li","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_9","first-page":"2839","article-title":"New automated machine learning based imbalanced learning method for fault diagnosis","volume":"27","author":"Sun","year":"2021","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106925","DOI":"10.1016\/j.knosys.2021.106925","article-title":"A novel deep metric learning model for imbalanced fault diagnosis and toward open-set classification","volume":"220","author":"Wang","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.ins.2019.11.004","article-title":"Data imbalance in classification: Experimental evaluation","volume":"513","author":"Thabtah","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tang, M.Z., Liang, Z.X., Wu, H.W., and Wang, Z.M. (2021). Fault diagnosis method for wind turbine gearboxes based on IWOA-RF. Energies, 14.","DOI":"10.3390\/en14196283"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jmsy.2018.04.005","article-title":"Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning","volume":"48","author":"Zhang","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107043","DOI":"10.1016\/j.asoc.2020.107043","article-title":"New imbalanced bearing fault diagnosis method based on Sample-characteristic Oversampling TechniquE (SCOTE) and multi-class LS-SVM","volume":"101","author":"Wei","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_16","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the 28th Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9515","DOI":"10.1109\/ACCESS.2018.2890693","article-title":"Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: A comparative study","volume":"7","author":"Mao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108522","DOI":"10.1016\/j.measurement.2020.108522","article-title":"Improved generative adversarial network for vibration-based fault diagnosis with imbalanced data","volume":"169","author":"Zhao","year":"2021","journal-title":"Measurement"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107175","DOI":"10.1016\/j.ymssp.2020.107175","article-title":"Oversampling adversarial network for class-imbalanced fault diagnosis","volume":"149","author":"Zareapoor","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kim, T., and Chai, J. (2021). Pre-Processing Method to Improve Cross-Domain Fault Diagnosis for Bearing. Sensors, 21.","DOI":"10.3390\/s21154970"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8374","DOI":"10.1109\/JSEN.2019.2949057","article-title":"Knowledge transfer for rotary machine fault diagnosis","volume":"20","author":"Yan","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.knosys.2017.11.019","article-title":"A selective multiple instance transfer learning method for text categorization problems","volume":"141","author":"Liu","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8430","DOI":"10.1109\/TIE.2021.3108726","article-title":"Subdomain adaptation transfer learning network for fault diagnosis of roller bearings","volume":"69","author":"Wang","year":"2022","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_24","first-page":"3262854","article-title":"Intelligent Machinery Fault Diagnosis with Event-Based Camera","volume":"2023","author":"Li","year":"2023","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1109\/TII.2019.2917233","article-title":"Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network","volume":"16","author":"Chen","year":"2020","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6111","DOI":"10.1007\/s00521-019-04097-w","article-title":"A transfer convolutional neural network for fault diagnosis based on ResNet-50","volume":"32","author":"Wen","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_27","unstructured":"Jia, D., Wei, D., Socher, R., Li-Jia, L., Kai, L., and Li, F.-F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA."},{"key":"ref_28","unstructured":"Guyon, I., von Luxburg, U., Bengio, S., Wallach, H.M., Fergus, R., Vishwanathan, S.V.N., and Garnett, R. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3119137","DOI":"10.1109\/TIM.2021.3119137","article-title":"Rotating machinery fault diagnosis through a transformer convolution network subjected to transfer learning","volume":"70","author":"Pei","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108616","DOI":"10.1016\/j.ymssp.2021.108616","article-title":"A novel time\u2013frequency Transformer based on self\u2013attention mechanism and its application in fault diagnosis of rolling bearings","volume":"168","author":"Ding","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tang, X.Y., Xu, Z.B., and Wang, Z.G. (2022). A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model. Sensors, 22.","DOI":"10.3390\/s22103878"},{"key":"ref_32","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020, January 26\u201330). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. Proceedings of the International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.acha.2010.08.002","article-title":"Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool","volume":"30","author":"Daubechies","year":"2011","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_36","unstructured":"Hendrycks, D., and Gimpel, K. (2016). Gaussian error linear units (GELUs). arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/TPAMI.2018.2846566","article-title":"Fine-tuning cnn image retrieval with no human annotation","volume":"41","author":"Radenovic","year":"2019","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_38","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_39","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":"IEEE Trans. Ind. Inf."},{"key":"ref_40","unstructured":"Loshchilov, I., and Hutter, F. (2019). Decoupled Weight Decay Regularization, Cornell University Library."},{"key":"ref_41","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"104279","DOI":"10.1016\/j.engappai.2021.104279","article-title":"A novel deep autoencoder and hyperparametric adaptive learning for imbalance intelligent fault diagnosis of rotating machinery","volume":"102","author":"Li","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"105971","DOI":"10.1016\/j.knosys.2020.105971","article-title":"Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions","volume":"199","author":"Zhao","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_44","first-page":"1615676","article-title":"An Improved Bearing Fault Diagnosis Model of Variational Mode Decomposition Based on Linked Extension Neural Network","volume":"2022","author":"Wang","year":"2022","journal-title":"Comput. Intell. Neurosc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"112143","DOI":"10.1016\/j.measurement.2022.112143","article-title":"Intelligent rolling bearing imbalanced fault diagnosis based on Mel-Frequency Cepstrum Coefficient and Convolutional Neural Networks","volume":"205","author":"Yao","year":"2022","journal-title":"Measurement"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7431\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:39:18Z","timestamp":1760128758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,25]]},"references-count":45,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23177431"],"URL":"https:\/\/doi.org\/10.3390\/s23177431","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,8,25]]}}}