{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T04:46:59Z","timestamp":1775710019967,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62263021"],"award-info":[{"award-number":["No. 62263021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62263021"],"award-info":[{"award-number":["No. 62263021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Gansu Province college industry support plan Project","award":["2023CYZC-24"],"award-info":[{"award-number":["2023CYZC-24"]}]},{"name":"Gansu Province college industry support plan Project","award":["2023CYZC-24"],"award-info":[{"award-number":["2023CYZC-24"]}]},{"name":"Gansu Province Science and Technology Special Project","award":["No. 21YF5GA072"],"award-info":[{"award-number":["No. 21YF5GA072"]}]},{"name":"Gansu Province Science and Technology Special Project","award":["No. 21YF5GA072"],"award-info":[{"award-number":["No. 21YF5GA072"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s11760-024-03456-y","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T14:07:20Z","timestamp":1723126040000},"page":"8131-8148","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Bearing fault diagnosis method based on multi-domain feature fusion and heterogeneous network under small sample conditions"],"prefix":"10.1007","volume":"18","author":[{"given":"Xiaoqiang","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"key":"3456_CR1","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s42791-019-0016-y","volume":"1","author":"M Hamadache","year":"2019","unstructured":"Hamadache, M., Jung, J.H., Park, J., et al.: A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning. JMST Adv. 1, 125\u2013151 (2019)","journal-title":"JMST Adv."},{"key":"3456_CR2","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.ymssp.2018.02.016","volume":"108","author":"R Liu","year":"2018","unstructured":"Liu, R., Yang, B., Zio, E., et al.: Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech. Syst. Signal Process. 108, 33\u201347 (2018)","journal-title":"Mech. Syst. Signal Process."},{"key":"3456_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.107233","volume":"150","author":"W Mao","year":"2021","unstructured":"Mao, W., Feng, W., Liu, Y., et al.: A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis. Mech. Syst. Signal Process. 150, 107233 (2021)","journal-title":"Mech. Syst. Signal Process."},{"issue":"13","key":"3456_CR4","doi-asserted-by":"publisher","first-page":"2826","DOI":"10.3390\/electronics12132826","volume":"12","author":"Y Zhao","year":"2023","unstructured":"Zhao, Y., Hao, H., Chen, Y., et al.: Novelty detection and fault diagnosis method for bearing faults based on the hybrid deep autoencoder network. Electronics 12(13), 2826 (2023)","journal-title":"Electronics"},{"issue":"6","key":"3456_CR5","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ac543a","volume":"33","author":"H Zhao","year":"2022","unstructured":"Zhao, H., Yang, X., Chen, B., et al.: Bearing fault diagnosis using transfer learning and optimized deep belief network. Meas. Sci. Technol. 33(6), 065009 (2022)","journal-title":"Meas. Sci. Technol."},{"issue":"2","key":"3456_CR6","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/s42417-022-00595-9","volume":"11","author":"Z Jin","year":"2023","unstructured":"Jin, Z., Sun, Y.: Bearing fault diagnosis based on VMD fuzzy entropy and improved deep belief networks. J. Vib. Eng. Technol. 11(2), 577\u2013587 (2023)","journal-title":"J. Vib. Eng. Technol."},{"key":"3456_CR7","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.isatra.2019.11.010","volume":"100","author":"Z An","year":"2020","unstructured":"An, Z., Li, S., Wang, J., et al.: A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network. ISA Trans. 100, 155\u2013170 (2020)","journal-title":"ISA Trans."},{"issue":"12","key":"3456_CR8","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/acf598","volume":"34","author":"G Fu","year":"2023","unstructured":"Fu, G., Wei, Q., Yang, Y., et al.: Bearing fault diagnosis based on CNN-BiLSTM and residual module. Meas. Sci. Technol. 34(12), 125050 (2023)","journal-title":"Meas. Sci. Technol."},{"key":"3456_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.101877","volume":"55","author":"D Ruan","year":"2023","unstructured":"Ruan, D., Wang, J., Yan, J., et al.: CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis. Adv. Eng. Inform. 55, 101877 (2023)","journal-title":"Adv. Eng. Inform."},{"issue":"1","key":"3456_CR10","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s11668-022-01567-7","volume":"23","author":"Z Jin","year":"2023","unstructured":"Jin, Z., Chen, D., He, D., et al.: Bearing fault diagnosis based on VMD and improved CNN. J. Fail. Anal. Prev. 23(1), 165\u2013175 (2023)","journal-title":"J. Fail. Anal. Prev."},{"key":"3456_CR11","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.isatra.2018.04.005","volume":"77","author":"H Liu","year":"2018","unstructured":"Liu, H., Zhou, J., Zheng, Y., et al.: Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Trans. 77, 167\u2013178 (2018)","journal-title":"ISA Trans."},{"issue":"11","key":"3456_CR12","doi-asserted-by":"publisher","first-page":"3701","DOI":"10.1007\/s00170-021-07385-9","volume":"124","author":"T Jin","year":"2023","unstructured":"Jin, T., Yan, C., Chen, C., et al.: New domain adaptation method in shallow and deep layers of the CNN for bearing fault diagnosis under different working conditions. Int. J. Adv. Manuf. Technol. 124(11), 3701\u20133712 (2023)","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"3","key":"3456_CR13","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1109\/TFUZZ.2022.3186181","volume":"31","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Oh, S.K., Qiu, J., et al.: Reinforced two-stream fuzzy neural networks architecture realized with the aid of one-dimensional\/two-dimensional data features. IEEE Trans. Fuzzy Syst. 31(3), 707\u2013721 (2022)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"3456_CR14","doi-asserted-by":"crossref","unstructured":"Pandarakone S E, Masuko M, Mizuno Y, et al. Deep neural network based bearing fault diagnosis of induction motor using fast Fourier transform analysis. In: 2018 IEEE energy conversion congress and exposition (ECCE). IEEE, pp. 3214\u20133221 (2018)","DOI":"10.1109\/ECCE.2018.8557651"},{"key":"3456_CR15","doi-asserted-by":"publisher","first-page":"5189","DOI":"10.1007\/s12206-018-1017-8","volume":"32","author":"S Wan","year":"2018","unstructured":"Wan, S., Zhang, X., Dou, L.: Compound fault diagnosis of bearings using improved fast spectral kurtosis with VMD. J. Mech. Sci. Technol. 32, 5189\u20135199 (2018)","journal-title":"J. Mech. Sci. Technol."},{"issue":"2","key":"3456_CR16","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1109\/TEC.2017.2661541","volume":"32","author":"E Elbouchikhi","year":"2017","unstructured":"Elbouchikhi, E., Choqueuse, V., Amirat, Y., et al.: An efficient Hilbert-Huang transform-based bearing faults detection in induction machines. IEEE Trans. Energy Convers. 32(2), 401\u2013413 (2017)","journal-title":"IEEE Trans. Energy Convers."},{"issue":"8","key":"3456_CR17","doi-asserted-by":"publisher","first-page":"3747","DOI":"10.1007\/s42417-022-00780-w","volume":"11","author":"N Diao","year":"2023","unstructured":"Diao, N., Wang, Z., Ma, H., et al.: Fault diagnosis of rolling bearing under variable working conditions based on CWT and T-ResNet. J. Vib. Eng. Technol. 11(8), 3747\u20133757 (2023)","journal-title":"J. Vib. Eng. Technol."},{"issue":"11","key":"3456_CR18","doi-asserted-by":"publisher","first-page":"7286","DOI":"10.1016\/j.jfranklin.2020.04.024","volume":"357","author":"H Tao","year":"2020","unstructured":"Tao, H., Wang, P., Chen, Y., et al.: An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks. J. Franklin Inst. 357(11), 7286\u20137307 (2020)","journal-title":"J. Franklin Inst."},{"issue":"10","key":"3456_CR19","doi-asserted-by":"publisher","first-page":"3936","DOI":"10.3390\/s22103936","volume":"22","author":"J Yan","year":"2022","unstructured":"Yan, J., Kan, J., Luo, H.: Rolling bearing fault diagnosis based on Markov transition field and residual network. Sensors 22(10), 3936 (2022)","journal-title":"Sensors"},{"key":"3456_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.109076","volume":"232","author":"R Bai","year":"2023","unstructured":"Bai, R., Meng, Z., Xu, Q., et al.: Fractional Fourier and time domain recurrence plot fusion combining convolutional neural network for bearing fault diagnosis under variable working conditions. Reliab. Eng. Syst. Saf. 232, 109076 (2023)","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"5","key":"3456_CR21","doi-asserted-by":"publisher","first-page":"1851","DOI":"10.55730\/1300-0632.3909","volume":"30","author":"Y Kaya","year":"2022","unstructured":"Kaya, Y., Kuncan, F., Ertun\u00e7, H.M.: A new automatic bearing fault size diagnosis using time-frequency images of CWT and deep transfer learning methods. Turk. J. Electr. Eng. Comput. Sci. 30(5), 1851\u20131867 (2022)","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"issue":"2","key":"3456_CR22","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1007\/s11668-023-01616-9","volume":"23","author":"Q Zhang","year":"2023","unstructured":"Zhang, Q., Deng, L.: An intelligent fault diagnosis method of rolling bearings based on short-time Fourier transform and convolutional neural network. J. Fail. Anal. Prev. 23(2), 795\u2013811 (2023)","journal-title":"J. Fail. Anal. Prev."},{"issue":"4","key":"3456_CR23","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/acabdb","volume":"34","author":"W Fu","year":"2023","unstructured":"Fu, W., Jiang, X., Li, B., et al.: Rolling bearing fault diagnosis based on 2D time-frequency images and data augmentation technique. Meas. Sci. Technol. 34(4), 045005 (2023)","journal-title":"Meas. Sci. Technol."},{"issue":"1","key":"3456_CR24","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/acfe31","volume":"35","author":"D Yu","year":"2023","unstructured":"Yu, D., Fu, H., Song, Y., et al.: Deep transfer learning rolling bearing fault diagnosis method based on convolutional neural network feature fusion. Meas. Sci. Technol. 35(1), 015013 (2023)","journal-title":"Meas. Sci. Technol."},{"issue":"2","key":"3456_CR25","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3390\/lubricants11020074","volume":"11","author":"D Ruan","year":"2023","unstructured":"Ruan, D., Chen, X., G\u00fchmann, C., et al.: Improvement of generative adversarial network and its application in bearing fault diagnosis: a review. Lubricants 11(2), 74 (2023)","journal-title":"Lubricants"},{"issue":"10","key":"3456_CR26","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3390\/lubricants9100105","volume":"9","author":"D Ruan","year":"2021","unstructured":"Ruan, D., Song, X., G\u00fchmann, C., et al.: Collaborative optimization of CNN and GAN for bearing fault diagnosis under unbalanced datasets. Lubricants 9(10), 105 (2021)","journal-title":"Lubricants"},{"key":"3456_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109285","volume":"177","author":"Z Zheng","year":"2021","unstructured":"Zheng, Z., Fu, J., Lu, C., et al.: Research on rolling bearing fault diagnosis of small dataset based on a new optimal transfer learning network. Measurement 177, 109285 (2021)","journal-title":"Measurement"},{"key":"3456_CR28","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.ymssp.2015.04.021","volume":"64","author":"WA Smith","year":"2015","unstructured":"Smith, W.A., Randall, R.B.: Rolling element bearing diagnostics using the case Western Reserve University data: a benchmark study. Mech. Syst. Signal Process. 64, 100\u2013131 (2015)","journal-title":"Mech. Syst. Signal Process."},{"key":"3456_CR29","doi-asserted-by":"publisher","first-page":"66257","DOI":"10.1109\/ACCESS.2020.2985617","volume":"8","author":"M Qiao","year":"2020","unstructured":"Qiao, M., Yan, S., Tang, X., et al.: Deep convolutional and LSTM recurrent neural networks for rolling bearing fault diagnosis under strong noises and variable loads. IEEE Access 8, 66257\u201366269 (2020)","journal-title":"IEEE Access"},{"issue":"09","key":"3456_CR30","first-page":"23","volume":"54","author":"X Zhao","year":"2020","unstructured":"Zhao, X., Liang, H.: Fault diagnosis method for rolling bearing under variable working conditions using improved residual neural network. J. Xian Jiao Tong Univ. 54(09), 23\u201331 (2020)","journal-title":"J. Xian Jiao Tong Univ."},{"key":"3456_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.107936","volume":"160","author":"Y Qin","year":"2021","unstructured":"Qin, Y., Yao, Q., Wang, Y., et al.: Parameter sharing adversarial domain adaptation networks for fault transfer diagnosis of planetary gearboxes. Mech. Syst. Signal Process. 160, 107936 (2021)","journal-title":"Mech. Syst. Signal Process."},{"issue":"10","key":"3456_CR32","doi-asserted-by":"publisher","first-page":"3878","DOI":"10.3390\/s22103878","volume":"22","author":"X Tang","year":"2022","unstructured":"Tang, X., Xu, Z., Wang, Z.: A novel fault diagnosis method of rolling bearing based on integrated vision transformer model. Sensors 22(10), 3878 (2022)","journal-title":"Sensors"},{"key":"3456_CR33","doi-asserted-by":"crossref","unstructured":"Liu H, Wu G, Liu P, et al. Bearing fault diagnosis based on STFT-SPWVD and improved convolutional neural network. In: Machine Learning, Multi Agent and Cyber Physical Systems: Proceedings of the 15th International FLINS Conference (FLINS 2022), pp. 330\u2013338 (2023)","DOI":"10.1142\/9789811269264_0039"},{"key":"3456_CR34","doi-asserted-by":"crossref","unstructured":"Lv X, Li H. Rolling bearing fault diagnosis based on GWVD and convolutional neural network. In: International Conference on Intelligent Computing. Singapore: Springer Nature Singapore, pp. 514\u2013523 (2023)","DOI":"10.1007\/978-981-99-4761-4_44"},{"key":"3456_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2022.102659","volume":"122","author":"M Huang","year":"2023","unstructured":"Huang, M., Yin, J., Yan, S., et al.: A fault diagnosis method of bearings based on deep transfer learning. Simul. Model. Pract. Theory 122, 102659 (2023)","journal-title":"Simul. Model. Pract. Theory"},{"key":"3456_CR36","doi-asserted-by":"publisher","first-page":"60725","DOI":"10.1109\/ACCESS.2022.3180844","volume":"10","author":"S Noppitak","year":"2022","unstructured":"Noppitak, S., Surinta, O.: dropCyclic: snapshot ensemble convolutional neural network based on a new learning rate schedule for land use classification. IEEE Access 10, 60725\u201360737 (2022)","journal-title":"IEEE Access"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03456-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03456-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03456-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T13:47:07Z","timestamp":1726235227000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03456-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,8]]},"references-count":36,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["3456"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03456-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4334445\/v1","asserted-by":"object"}]},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,8]]},"assertion":[{"value":"27 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}