{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T10:38:04Z","timestamp":1752230284722},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T00:00:00Z","timestamp":1714003200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T00:00:00Z","timestamp":1714003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"This work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region and the Research Foundation of Karamay.","award":["Grant No.2023D01F42"],"award-info":[{"award-number":["Grant No.2023D01F42"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s10586-024-04451-1","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T11:01:37Z","timestamp":1714042897000},"page":"9615-9634","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CSDANet: a new lightweight fault diagnosis framework towards heavy noise and small samples"],"prefix":"10.1007","volume":"27","author":[{"given":"Zhao","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyang","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YiWei","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenpei","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"issue":"1","key":"4451_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2307\/2184861","volume":"89","author":"P Kitcher","year":"1980","unstructured":"Kitcher, P.: A priori knowledge. Philos. Rev. 89(1), 3\u201323 (1980)","journal-title":"Philos. Rev."},{"issue":"4","key":"4451_CR2","doi-asserted-by":"publisher","first-page":"2226","DOI":"10.1109\/TII.2013.2243743","volume":"9","author":"X Dai","year":"2013","unstructured":"Dai, X., Gao, Z.: From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis. IEEE Trans. Ind. Inf. 9(4), 2226\u20132238 (2013)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"4451_CR3","doi-asserted-by":"publisher","first-page":"5898","DOI":"10.1016\/j.egyr.2022.04.043","volume":"8","author":"Y-Y Hong","year":"2022","unstructured":"Hong, Y.-Y., Pula, R.A.: Methods of photovoltaic fault detection and classification: A review. Energy Rep. 8, 5898\u20135929 (2022)","journal-title":"Energy Rep."},{"key":"4451_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2022.108718","volume":"192","author":"Y Zhu","year":"2022","unstructured":"Zhu, Y., Li, G., Tang, S., Wang, R., Su, H., Wang, C.: Acoustic signal-based fault detection of hydraulic piston pump using a particle swarm optimization enhancement cnn. Appl. Acoust. 192, 108718 (2022)","journal-title":"Appl. Acoust."},{"key":"4451_CR5","unstructured":"Patton, R.J.: Fault detection and diagnosis in aerospace systems using analytical redundancy, 1\u20131 (1990). IET"},{"issue":"3","key":"4451_CR6","doi-asserted-by":"publisher","first-page":"1781","DOI":"10.3390\/su14031781","volume":"14","author":"M-H Wang","year":"2022","unstructured":"Wang, M.-H., Lu, S.-D., Hsieh, C.-C., Hung, C.-C.: Fault detection of wind turbine blades using multi-channel cnn. Sustainability 14(3), 1781 (2022)","journal-title":"Sustainability"},{"key":"4451_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3238059","volume":"72","author":"JB Thomas","year":"2023","unstructured":"Thomas, J.B., Chaudhari, S.G., Shihabudheen, K., Verma, N.K.: Cnn-based transformer model for fault detection in power system networks. IEEE Trans. Instrum. Measurement 72, 1\u201310 (2023)","journal-title":"IEEE Trans. Instrum. Measurement"},{"key":"4451_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105794","volume":"119","author":"L Jia","year":"2023","unstructured":"Jia, L., Chow, T.W., Yuan, Y.: Gtfe-net: a gramian time frequency enhancement cnn for bearing fault diagnosis. Eng. Appl. Artif. Intell. 119, 105794 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4451_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105872","volume":"120","author":"A Choudhary","year":"2023","unstructured":"Choudhary, A., Mishra, R.K., Fatima, S., Panigrahi, B.: Multi-input cnn based vibro-acoustic fusion for accurate fault diagnosis of induction motor. Eng. Appl. Artif. Intell. 120, 105872 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"4451_CR10","doi-asserted-by":"publisher","DOI":"10.1088\/2631-8695\/acae1d","volume":"5","author":"M Gana","year":"2023","unstructured":"Gana, M., Achour, H., Laghrouche, M.: Enhanced motor fault detection system based on a dual-signature image classification method using cnn. Eng. Res. Exp. 5(1), 015009 (2023)","journal-title":"Eng. Res. Exp."},{"key":"4451_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.122071","volume":"353","author":"B Gong","year":"2024","unstructured":"Gong, B., An, A., Shi, Y., Zhang, X.: Fast fault detection method for photovoltaic arrays with adaptive deep multiscale feature enhancement. Appl. Energy 353, 122071 (2024)","journal-title":"Appl. Energy"},{"key":"4451_CR12","doi-asserted-by":"publisher","first-page":"104029","DOI":"10.1109\/ACCESS.2021.3099124","volume":"9","author":"MA Habib","year":"2021","unstructured":"Habib, M.A., Hasan, M.J., Kim, J.-M.: A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals. IEEE Access 9, 104029\u2013104050 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3099124","journal-title":"IEEE Access"},{"key":"4451_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2023.3256975","author":"MF Guo","year":"2023","unstructured":"Guo, M.F., Liu, W.L., Gao, J.H., Chen, D.Y.: A data-enhanced high impedance fault detection method under imbalanced sample scenarios in distribution networks. IEEE Trans. Ind. Appl. (2023). https:\/\/doi.org\/10.1109\/TIA.2023.3256975","journal-title":"IEEE Trans. Ind. Appl."},{"key":"4451_CR14","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/j.isatra.2022.10.031","volume":"136","author":"X Fang","year":"2023","unstructured":"Fang, X., Qu, J., Chai, Y., Liu, B.: Adaptive multiscale and dual subnet convolutional auto-encoder for intermittent fault detection of analog circuits in noise environment. ISA Trans. 136, 428\u2013441 (2023)","journal-title":"ISA Trans."},{"key":"4451_CR15","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1016\/j.psep.2022.12.055","volume":"170","author":"K Zhou","year":"2023","unstructured":"Zhou, K., Tong, Y., Li, X., Wei, X., Huang, H., Song, K., Chen, X.: Exploring global attention mechanism on fault detection and diagnosis for complex engineering processes. Process Saf. Environ. Prot. 170, 660\u2013669 (2023)","journal-title":"Process Saf. Environ. Prot."},{"key":"4451_CR16","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.03762","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141, Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Proc. Syst. (2017). https:\/\/doi.org\/10.48550\/arXiv.1706.03762","journal-title":"Adv. Neural Inf. Proc. Syst."},{"key":"4451_CR17","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"4451_CR18","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1290\u20131299 (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"4451_CR19","doi-asserted-by":"publisher","first-page":"9325","DOI":"10.1109\/ACCESS.2020.2964540","volume":"8","author":"X Nie","year":"2020","unstructured":"Nie, X., Duan, M., Ding, H., Hu, B., Wong, E.K.: Attention mask r-cnn for ship detection and segmentation from remote sensing images. Ieee Access 8, 9325\u20139334 (2020)","journal-title":"Ieee Access"},{"key":"4451_CR20","doi-asserted-by":"crossref","unstructured":"Fan, Z., Gong, Y., Liu, D., Wei, Z., Wang, S., Jiao, J., Duan, N., Zhang, R., Huang, X.: Mask attention networks: Rethinking and strengthen transformer. arXiv preprint arXiv:2103.13597 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.135"},{"key":"4451_CR21","doi-asserted-by":"crossref","unstructured":"Harley, A.W., Derpanis, K.G., Kokkinos, I.: Segmentation-aware convolutional networks using local attention masks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5038\u20135047 (2017)","DOI":"10.1109\/ICCV.2017.539"},{"key":"4451_CR22","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"4451_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"4451_CR24","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 510\u2013519 (2019)","DOI":"10.1109\/CVPR.2019.00060"},{"issue":"2","key":"4451_CR25","doi-asserted-by":"publisher","first-page":"e24122","DOI":"10.1097\/MD.0000000000024122","volume":"100","author":"Y Kang","year":"2021","unstructured":"Kang, Y., Ku, E.J., Jung, I.G., Kang, M.H., Choi, Y.-S., Jung, H.J.: Dexamethasone and post-adenotonsillectomy pain in children: double-blind, randomized controlled trial. Medicine 100(2), e24122 (2021)","journal-title":"Medicine"},{"key":"4451_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107031","volume":"126","author":"J Wang","year":"2023","unstructured":"Wang, J., Shao, H., Yan, S., Liu, B.: C-ecaformer: a new lightweight fault diagnosis framework towards heavy noise and small samples. Eng. Appl. Artif. Intell. 126, 107031 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4451_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.110500","volume":"189","author":"J Li","year":"2022","unstructured":"Li, J., Liu, Y., Li, Q.: Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method. Measurement 189, 110500 (2022)","journal-title":"Measurement"},{"key":"4451_CR28","unstructured":"Liu, Z., Sun, M., Zhou, T., Huang, G., Darrell, T.: Rethinking the value of network pruning. arXiv preprint arXiv:1810.05270 (2018)"},{"key":"4451_CR29","doi-asserted-by":"crossref","unstructured":"Cho, J.H., Hariharan, B.: On the efficacy of knowledge distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4794\u20134802 (2019)","DOI":"10.1109\/ICCV.2019.00489"},{"issue":"7459","key":"4451_CR30","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1038\/nature12314","volume":"499","author":"M Kaltenbrunner","year":"2013","unstructured":"Kaltenbrunner, M., Sekitani, T., Reeder, J., Yokota, T., Kuribara, K., Tokuhara, T., Drack, M., Schw\u00f6diauer, R., Graz, I., Bauer-Gogonea, S.: An ultra-lightweight design for imperceptible plastic electronics. Nature 499(7459), 458\u2013463 (2013)","journal-title":"Nature"},{"issue":"1","key":"4451_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 1\u201340 (2016)","journal-title":"J. Big Data"},{"key":"4451_CR32","unstructured":"Zhang, T., Wu, F., Katiyar, A., Weinberger, K.Q., Artzi, Y.: Revisiting few-sample bert fine-tuning. arXiv preprint arXiv:2006.05987 (2020)"},{"key":"4451_CR33","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.compind.2019.01.001","volume":"106","author":"S Shao","year":"2019","unstructured":"Shao, S., Wang, P., Tan, R.: Generative adversarial networks for data augmentation in machine fault diagnosis. Comput. Ind. 106, 85\u201393 (2019)","journal-title":"Comput. Ind."},{"key":"4451_CR34","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","volume":"173","author":"T Kattenborn","year":"2021","unstructured":"Kattenborn, T., Leitloff, J., Schiefer, F., Hinz, S.: Review on convolutional neural networks (cnn) in vegetation remote sensing. ISPRS J. Photogramm. Remote Sens. 173, 24\u201349 (2021)","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"4451_CR35","doi-asserted-by":"crossref","unstructured":"Fang, H.R., Deng, J., Chen, D.S., Jiang, W.J., Shao, S., Tang, M.C., Liu, J.J.: You can get smaller: A lightweight self-activation convolution unit modified by transformer for fault diagnosis. Adv. Eng. Inform. 55, 101890 (2023)","DOI":"10.1016\/j.aei.2023.101890"},{"key":"4451_CR36","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 Proc. 64, 100\u2013131 (2015)","journal-title":"Mech. Syst. Signal Proc."},{"key":"4451_CR37","doi-asserted-by":"crossref","unstructured":"Song, R., Ai, Y., Tian, B., Chem, L., Zhu, F., Fei, Y.: MSCFNet: a lightweight network with multi-scale context fusion for real-time semantic segmentation. IEEE Trans. Intell. Transp. Syst. 23(12) 25489\u201325499","DOI":"10.1109\/TITS.2021.3098355"},{"key":"4451_CR38","first-page":"1","volume":"71","author":"H Fang","year":"2021","unstructured":"Fang, H., Deng, J., Bai, Y., Feng, B., Li, S., Shao, S., Chen, D.: Clformer: a lightweight transformer based on convolutional embedding and linear self-attention with strong robustness for bearing fault diagnosis under limited sample conditions. IEEE Trans. Instrum. Measurement 71, 1\u20138 (2021)","journal-title":"IEEE Trans. Instrum. Measurement"},{"key":"4451_CR39","unstructured":"Mehta, S., Rastegari, M.: Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv preprint arXiv:2110.02178 (2021)"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04451-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04451-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04451-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T19:25:54Z","timestamp":1725909954000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04451-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,25]]},"references-count":39,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["4451"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04451-1","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2024,4,25]]},"assertion":[{"value":"8 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 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 have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}