{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:19:18Z","timestamp":1743009558890,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819756742"},{"type":"electronic","value":"9789819756759"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-5675-9_10","type":"book-chapter","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T01:10:40Z","timestamp":1722474640000},"page":"107-118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SeqAttention-Net: Design of a Deep Neural Network for Bearing Fault Detection Based on Small Sample Datasets"],"prefix":"10.1007","author":[{"given":"Haifeng","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengliang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","first-page":"108968","DOI":"10.1016\/j.ress.2022.108968","volume":"230","author":"ZH Liu","year":"2023","unstructured":"Liu, Z.H., Chen, L., Wei, H.L., et al.: A tensor-based domain alignment method for intelligent fault diagnosis of rolling bearing in rotating machinery. Reliab. Eng. Syst. Saf. 230, 108968 (2023)","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, Y., Yao, J., et al.: Multi-sensor fusion fault diagnosis method of wind turbine bearing based on adaptive convergent viewable neural networks. Reliab. Eng. Syst. Saf. 245, 109980 (2024)","DOI":"10.1016\/j.ress.2024.109980"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Liu, R., Xiao, D., Lin, D., et al.: Intelligent bearing anomaly detection for industrial Internet of Things based on auto-encoder Wasserstein generative adversarial network. IEEE Internet Things J. 1, 22869 (2024)","DOI":"10.1109\/JIOT.2024.3358871"},{"key":"10_CR4","doi-asserted-by":"publisher","first-page":"123225","DOI":"10.1016\/j.eswa.2024.123225","volume":"247","author":"L Cui","year":"2024","unstructured":"Cui, L., Jiang, Z., Liu, D., et al.: A novel adaptive generalized domain data fusion-driven kernel sparse representation classification method for intelligent bearing fault diagnosis. Expert Syst. Appl. 247, 123225 (2024)","journal-title":"Expert Syst. Appl."},{"key":"10_CR5","doi-asserted-by":"publisher","first-page":"106507","DOI":"10.1016\/j.engappai.2023.106507","volume":"124","author":"Y Hou","year":"2023","unstructured":"Hou, Y., Wang, J., Chen, Z., et al.: DiagnosisFormer: an efficient rolling bearing fault diagnosis method based on improved transformer. Eng. Appl. Artif. Intell. 124, 106507 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"2","key":"10_CR6","doi-asserted-by":"publisher","first-page":"e13360","DOI":"10.1111\/exsy.13360","volume":"41","author":"AR Sahu","year":"2024","unstructured":"Sahu, A.R., Palei, S.K., Mishra, A.: Data-driven fault diagnosis approaches for industrial equipment: a review. Expert. Syst. 41(2), e13360 (2024)","journal-title":"Expert. Syst."},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Wang, B., Li, H.M., Hu, X., et al.: Rolling bearing fault diagnosis based on multi-domain features and whale optimized support vector machine. J. Vibration Control 10775463241231344 (2024)","DOI":"10.1177\/10775463241231344"},{"key":"10_CR8","doi-asserted-by":"publisher","first-page":"102304","DOI":"10.1016\/j.aei.2023.102304","volume":"59","author":"B Pang","year":"2024","unstructured":"Pang, B., Liu, Q., Sun, Z., et al.: Time-frequency supervised contrastive learning via pseudo-labeling: an unsupervised domain adaptation network for rolling bearing fault diagnosis under time-varying speeds. Adv. Eng. Inform. 59, 102304 (2024)","journal-title":"Adv. Eng. Inform."},{"key":"10_CR9","doi-asserted-by":"publisher","first-page":"111037","DOI":"10.1016\/j.ymssp.2023.111037","volume":"208","author":"Z Yang","year":"2024","unstructured":"Yang, Z., Wu, B., Shao, J., et al.: Fault detection of high-speed train axle bearings based on a hybridized physical and data-driven temperature model. Mech. Syst. Signal Process. 208, 111037 (2024)","journal-title":"Mech. Syst. Signal Process."},{"key":"10_CR10","doi-asserted-by":"publisher","first-page":"107138","DOI":"10.1016\/j.engappai.2023.107138","volume":"127","author":"H Tang","year":"2024","unstructured":"Tang, H., Tang, Y., Su, Y., et al.: Feature extraction of multi-sensors for early bearing fault diagnosis using deep learning based on minimum unscented Kalman filter. Eng. Appl. Artif. Intell. 127, 107138 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10_CR11","doi-asserted-by":"publisher","first-page":"111205","DOI":"10.1016\/j.knosys.2023.111205","volume":"283","author":"Y Xue","year":"2024","unstructured":"Xue, Y., Wen, C., Wang, Z., et al.: A novel framework for motor bearing fault diagnosis based on multi-transformation domain and multi-source data. Knowl.-Based Syst. 283, 111205 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Tang, Y., Zhang, C., Wu, J., et al.: Deep learning-based bearing fault diagnosis using a trusted multi-scale quadratic attention-embedded convolutional neural network. IEEE Trans. Instrum. Meas. 73, 1\u201315 (2024)","DOI":"10.1109\/TIM.2024.3374311"},{"key":"10_CR13","doi-asserted-by":"publisher","first-page":"122365","DOI":"10.1016\/j.eswa.2023.122365","volume":"239","author":"J Ma","year":"2024","unstructured":"Ma, J., Hu, S., Fu, J., et al.: A hierarchical attention detector for bearing surface defect detection. Expert Syst. Appl. 239, 122365 (2024)","journal-title":"Expert Syst. Appl."},{"key":"10_CR14","doi-asserted-by":"publisher","first-page":"110098","DOI":"10.1016\/j.ymssp.2023.110098","volume":"189","author":"Z Wu","year":"2023","unstructured":"Wu, Z., Jiang, H., Zhu, H., et al.: A knowledge dynamic matching unit-guided multi-source domain adaptation network with attention mechanism for rolling bearing fault diagnosis. Mech. Syst. Signal Process. 189, 110098 (2023)","journal-title":"Mech. Syst. Signal Process."},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Xue, L., Lei, C., Jiao, M., et al.: Rolling bearing fault diagnosis method based on self-calibrated coordinate attention mechanism and multi-scale convolutional neural network under small samples. IEEE Sens. J. 23, 10206\u201310214 (2023)","DOI":"10.1109\/JSEN.2023.3260208"},{"key":"10_CR16","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.isatra.2022.06.035","volume":"133","author":"S Zhang","year":"2023","unstructured":"Zhang, S., Liu, Z., Chen, Y., et al.: Selective kernel convolution deep residual network based on channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis. ISA Trans. 133, 369\u2013383 (2023)","journal-title":"ISA Trans."},{"key":"10_CR17","first-page":"15908","volume":"34","author":"K Han","year":"2021","unstructured":"Han, K., Xiao, A., Wu, E., et al.: Transformer in transformer. Adv. Neural. Inf. Process. Syst. 34, 15908\u201315919 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR18","unstructured":"Yu, H., Huang, J., Li, L., et al.: Deep fractional Fourier transform. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"issue":"2","key":"10_CR19","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s40430-023-04645-5","volume":"46","author":"F Gougam","year":"2024","unstructured":"Gougam, F., Afia, A., Soualhi, A., et al.: Bearing faults classification using a new approach of signal processing combined with machine learning algorithms. J. Braz. Soc. Mech. Sci. Eng. 46(2), 65 (2024)","journal-title":"J. Braz. Soc. Mech. Sci. Eng."},{"key":"10_CR20","doi-asserted-by":"publisher","first-page":"101480","DOI":"10.1016\/j.aei.2021.101480","volume":"51","author":"X Li","year":"2022","unstructured":"Li, X., Jiang, H., Xie, M., et al.: A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with multi-source domains. Adv. Eng. Inform. 51, 101480 (2022)","journal-title":"Adv. Eng. Inform."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5675-9_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T01:13:19Z","timestamp":1722474799000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5675-9_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819756742","9789819756759"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5675-9_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2024\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}