{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:44:45Z","timestamp":1784249085258,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":36,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789811659393","type":"print"},{"value":"9789811659409","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-5940-9_5","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T23:04:08Z","timestamp":1631228648000},"page":"65-79","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Transformer Model-Based Approach to Bearing Fault Diagnosis"],"prefix":"10.1007","author":[{"given":"Zhenshan","family":"Bao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialei","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.triboint.2015.12.037","volume":"96","author":"A Rai","year":"2016","unstructured":"Rai, A., Upadhyay, S.H.: A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribol. Int. 96, 289\u2013306 (2016)","journal-title":"Tribol. Int."},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.ymssp.2017.06.012","volume":"99","author":"M Cerrada","year":"2018","unstructured":"Cerrada, M., et al.: A review on data-driven fault severity assessment in rolling bearings. Mech. Syst. Signal Process. 99, 169\u2013196 (2018)","journal-title":"Mech. Syst. Signal Process."},{"issue":"1","key":"5_CR3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1049\/hve.2016.0005","volume":"1","author":"K Chen","year":"2016","unstructured":"Chen, K., Huang, C., He, J.: Fault detection, classification and location for transmission lines and distribution systems: a review on the methods. High Voltage 1(1), 25\u201333 (2016)","journal-title":"High Voltage"},{"issue":"17","key":"5_CR4","doi-asserted-by":"publisher","first-page":"1886","DOI":"10.1109\/LPT.2016.2574800","volume":"28","author":"FN Khan","year":"2016","unstructured":"Khan, F.N., et al.: Modulation format identification in coherent receivers using deep machine learning. IEEE Photon. Technol. Lett. 28(17), 1886\u20131889 (2016)","journal-title":"IEEE Photon. Technol. Lett."},{"issue":"2","key":"5_CR5","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1109\/TII.2016.2605629","volume":"13","author":"H Zhang","year":"2016","unstructured":"Zhang, H., et al.: Object-level video advertising: an optimization framework. IEEE Trans. Ind. Inform. 13(2), 520\u2013531 (2016)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"2","key":"5_CR6","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1109\/TII.2016.2601521","volume":"13","author":"H Zhang","year":"2016","unstructured":"Zhang, H., et al.: Understanding subtitles by character-level sequence-to-sequence learning. IEEE Trans. Ind. Inform. 13(2), 616\u2013624 (2016)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"6","key":"5_CR7","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s00521-016-2501-7","volume":"29","author":"H Ali","year":"2018","unstructured":"Ali, H., et al.: Speaker recognition with hybrid features from a deep belief network. Neural Comput. Appl. 29(6), 13\u201319 (2018)","journal-title":"Neural Comput. Appl."},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Muhammad, U.R., et al.: Goal-driven sequential data abstraction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 71\u201380 (2019)","DOI":"10.1109\/ICCV.2019.00016"},{"issue":"1","key":"5_CR9","first-page":"11","volume":"54","author":"FY Liu","year":"2018","unstructured":"Liu, F.Y., Wang, S.H., Zhang, Y.D.: Survey on deep belief network model and its applications. Comput. Eng. Appl. 54(1), 11\u201318 (2018)","journal-title":"Comput. Eng. Appl."},{"issue":"8","key":"5_CR10","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","volume":"53","author":"A Khan","year":"2020","unstructured":"Khan, A., et al.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53(8), 5455\u20135516 (2020)","journal-title":"Artif. Intell. Rev."},{"key":"5_CR11","first-page":"23","volume":"21","author":"A Karpathy","year":"2015","unstructured":"Karpathy, A.: The unreasonable effectiveness of recurrent neural networks. Andrej Karpathy Blog 21, 23 (2015)","journal-title":"Andrej Karpathy Blog"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Ma, L., et al.: Bearing fault diagnosis based on convolutional neural network learning of time-domain vibration signal imaging. In: 2019 Chinese Control and Decision Conference (CCDC). IEEE, pp. 659\u2013664 (2019)","DOI":"10.1109\/CCDC.2019.8832909"},{"key":"5_CR13","doi-asserted-by":"publisher","first-page":"106627","DOI":"10.1016\/j.knosys.2020.106627","volume":"212","author":"Y Liu","year":"2021","unstructured":"Liu, Y., et al.: Hierarchical independence thresholding for learning Bayesian network classifiers. Knowl.-Based Syst. 212, 106627 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Sun, W., Paiva, A.R.C., Xu, P., Sundaram, A., Braatz, R.D.: Fault detection and identification using Bayesian recurrent neural networks. Comput. Chem. Eng. 141 (2020)","DOI":"10.1016\/j.compchemeng.2020.106991"},{"issue":"6","key":"5_CR15","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.3390\/app9061161","volume":"9","author":"X Chen","year":"2019","unstructured":"Chen, X., et al.: Rolling bearings fault diagnosis based on tree heuristic feature selection and the dependent feature vector combined with rough sets. Appl. Sci. 9(6), 1161 (2019)","journal-title":"Appl. Sci."},{"issue":"9","key":"5_CR16","first-page":"319","volume":"6","author":"T Tong","year":"2020","unstructured":"Tong, T., Xu, X.: Improvement of power system fault diagnosis algorithm based on Petri Net. Int. Core J. Eng. 6(9), 319\u2013334 (2020)","journal-title":"Int. Core J. Eng."},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"5_CR18","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.measurement.2016.04.007","volume":"89","author":"W Sun","year":"2016","unstructured":"Sun, W., et al.: A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 89, 171\u2013178 (2016)","journal-title":"Measurement"},{"key":"5_CR19","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.neucom.2015.06.008","volume":"168","author":"C Li","year":"2015","unstructured":"Li, C., et al.: Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 168, 119\u2013127 (2015)","journal-title":"Neurocomputing"},{"issue":"11","key":"5_CR20","doi-asserted-by":"publisher","first-page":"8760","DOI":"10.1109\/TIE.2018.2833045","volume":"66","author":"S Kiranyaz","year":"2018","unstructured":"Kiranyaz, S., et al.: Real-time fault detection and identification for MMC using 1-D convolutional neural networks. IEEE Trans. Industr. Electron. 66(11), 8760\u20138771 (2018)","journal-title":"IEEE Trans. Industr. Electron."},{"issue":"2","key":"5_CR21","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s11265-018-1378-3","volume":"91","author":"L Eren","year":"2019","unstructured":"Eren, L., Ince, T., Kiranyaz, S.: A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. J. Signal Process. Syst. 91(2), 179\u2013189 (2019)","journal-title":"J. Signal Process. Syst."},{"key":"5_CR22","doi-asserted-by":"publisher","first-page":"1308","DOI":"10.1016\/j.neucom.2017.09.069","volume":"275","author":"O Abdeljaber","year":"2018","unstructured":"Abdeljaber, O., et al.: 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomputing 275, 1308\u20131317 (2018)","journal-title":"Neurocomputing"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Eren, L.: Bearing fault detection by one-dimensional convolutional neural networks. Math. Probl. Eng., 34\u201337 (2017)","DOI":"10.1155\/2017\/8617315"},{"key":"5_CR24","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000\u20136010 (2017)"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. arXiv Preprint, arXiv:2012-15840 (2020)","DOI":"10.1109\/CVPR46437.2021.00681"},{"issue":"2","key":"5_CR26","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1109\/TIE.2017.2733438","volume":"65","author":"R Zhao","year":"2017","unstructured":"Zhao, R., et al.: Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Trans. Ind. Electron. 65(2), 1539\u20131548 (2017)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"26","key":"5_CR27","first-page":"429","volume":"93","author":"D Gabor","year":"1946","unstructured":"Gabor, D.: Theory of communication. Part 1: The analysis of information. J. Instit. Electr. Eng. Pt. III Radio Commun. Eng. 93(26), 429\u2013441 (1946)","journal-title":"J. Instit. Electr. Eng. Pt. III Radio Commun. Eng."},{"issue":"5","key":"5_CR28","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1016\/S0888-3270(03)00077-3","volume":"18","author":"X Lou","year":"2004","unstructured":"Lou, X., Loparo, K.A.: Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech. Syst. Signal Process. 18(5), 1077\u20131095 (2004)","journal-title":"Mech. Syst. Signal Process."},{"key":"5_CR29","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":"5_CR30","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv Preprint, arXiv:1412.6980 (2014)"},{"key":"5_CR31","unstructured":"Keskar, N.S., et al.: On large-batch training for deep learning: generalization gap and sharp minima. In: 5th International Conference on Learning Representations, ICLR 2017 (2019)"},{"key":"5_CR32","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.procs.2018.01.106","volume":"127","author":"AZ Hinchi","year":"2018","unstructured":"Hinchi, A.Z., Tkiouat, M.: Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network. Procedia Comput. Sci. 127, 123\u2013132 (2018)","journal-title":"Procedia Comput. Sci."},{"issue":"4","key":"5_CR33","first-page":"103","volume":"40","author":"Y Fan","year":"2020","unstructured":"Fan, Y., et al.: Study on a small sample rolling bearing fault diagnosis method based on BI-LSTM. Noise Vib. Control 40(4), 103\u2013108 (2020)","journal-title":"Noise Vib. Control"},{"key":"5_CR34","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.aei.2017.02.005","volume":"32","author":"C Lu","year":"2017","unstructured":"Lu, C., Wang, Z., Zhou, B.: Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification. Adv. Eng. Inform. 32, 139\u2013151 (2017)","journal-title":"Adv. Eng. Inform."},{"key":"5_CR35","doi-asserted-by":"crossref","unstructured":"Liang, T., et al.: Bearing fault diagnosis based on improved ensemble learning and deep belief network. In: Journal of Physics: Conference Series, vol. 1074, no. 1, p. 012154. IOP Publishing (2018)","DOI":"10.1088\/1742-6596\/1074\/1\/012154"},{"key":"5_CR36","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.ymssp.2015.10.025","volume":"72","author":"F Jia","year":"2016","unstructured":"Jia, F., et al.: Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech. Syst. Signal Process. 72, 303\u2013315 (2016)","journal-title":"Mech. Syst. Signal Process."}],"container-title":["Communications in Computer and Information Science","Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-5940-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:02:10Z","timestamp":1710255730000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-5940-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811659393","9789811659409"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-5940-9_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPCSEE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of Pioneering Computer Scientists, Engineers and Educators","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiyuan","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpcsee2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2021.icpcsee.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"256","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"81","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}