{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:24:23Z","timestamp":1742930663829,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030891336"},{"type":"electronic","value":"9783030891343"}],"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\/978-3-030-89134-3_3","type":"book-chapter","created":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T10:39:41Z","timestamp":1634467181000},"page":"25-36","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Bearing Fault Diagnosis Based on\u00a0Attentional Multi-scale CNN"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5669-1515","authenticated-orcid":false,"given":"Shuai","family":"Yang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7042-0188","authenticated-orcid":false,"given":"Yan","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9814-9521","authenticated-orcid":false,"given":"Xincheng","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Lixin","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"issue":"12","key":"3_CR1","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhang, B., Gao, D.: Bearing fault diagnosis base on multi-scale CNN and LSTM model. J. Intell. Manuf. 1\u201317 (2020)","DOI":"10.1007\/s10845-020-01600-2"},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/978-3-642-28768-8_5","volume-title":"Condition Monitoring of Machinery in Non-Stationary Operations","author":"M Cocconcelli","year":"2012","unstructured":"Cocconcelli, M., Zimroz, R., Rubini, R., Bartelmus, W.: STFT based approach for ball bearing fault detection in a varying speed motor. In: Fakhfakh, T., Bartelmus, W., Chaari, F., Zimroz, R., Haddar, M. (eds.) Condition Monitoring of Machinery in Non-Stationary Operations, pp. 41\u201350. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-28768-8_5"},{"key":"3_CR4","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":"3_CR5","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015)"},{"key":"3_CR6","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., Lei, Y., Lin, J., Zhou, X., Lu, N.: 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."},{"key":"3_CR7","unstructured":"Keskar, N.S., Mudigere, D., Nocedal, J., Smelyanskiy, M., Tang, P.T.P.: On large-batch training for deep learning: generalization gap and sharp minima. arXiv preprint arXiv:1609.04836 (2016)"},{"key":"3_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"3_CR9","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097\u20131105 (2012)"},{"issue":"1","key":"3_CR10","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s10845-013-0772-8","volume":"26","author":"H Li","year":"2013","unstructured":"Li, H., Lian, X., Guo, C., Zhao, P.: Investigation on early fault classification for rolling element bearing based on the optimal frequency band determination. J. Intell. Manuf. 26(1), 189\u2013198 (2013). https:\/\/doi.org\/10.1007\/s10845-013-0772-8","journal-title":"J. Intell. Manuf."},{"key":"3_CR11","doi-asserted-by":"publisher","unstructured":"Liang, M., Cao, P., Tang, J.: Rolling bearing fault diagnosis based on feature fusion with parallel convolutional neural network. Int. J. Adv. Manuf. Technol. (1), 819\u2013831 (2020). https:\/\/doi.org\/10.1007\/s00170-020-06401-8","DOI":"10.1007\/s00170-020-06401-8"},{"key":"3_CR12","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML (2010)"},{"key":"3_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"3_CR14","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/978-81-322-1602-5_35","volume-title":"Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012","author":"R Sharma","year":"2014","unstructured":"Sharma, R., Kumar, A., Kankar, P.K.: Ball bearing fault diagnosis using continuous wavelet transforms with modern algebraic function. In: Babu, B.V., et al. (eds.) Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. AISC, vol. 236, pp. 313\u2013322. Springer, New Delhi (2014). https:\/\/doi.org\/10.1007\/978-81-322-1602-5_35"},{"issue":"1","key":"3_CR15","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"issue":"10","key":"3_CR16","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1007\/s11265-019-01461-w","volume":"91","author":"D Wang","year":"2019","unstructured":"Wang, D., Guo, Q., Song, Y., Gao, S., Li, Y.: Application of multiscale learning neural network based on CNN in bearing fault diagnosis. J. Signal Process. Syst. 91(10), 1205\u20131217 (2019)","journal-title":"J. Signal Process. Syst."},{"key":"3_CR17","series-title":"Lecture Notes in Electrical Engineering","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1007\/978-981-15-2866-8_66","volume-title":"Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Qin, Y., Zhao, X., Zhang, S., Cheng, X.: Bearing fault diagnosis method based on graph Fourier transform and C4.5 decision tree. In: Qin, Y., Jia, L., Liu, B., Liu, Z., Diao, L., An, M. (eds.) EITRT 2019. LNEE, vol. 639, pp. 697\u2013705. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-2866-8_66"},{"key":"3_CR18","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1007\/978-3-030-57745-2_98","volume-title":"Advances in Asset Management and Condition Monitoring","author":"C Zhang","year":"2020","unstructured":"Zhang, C., et al.: Rolling element bearing fault diagnosis based on the wavelet packet transform and time-delay correlation demodulation analysis. In: Ball, A., Gelman, L., Rao, B.K.N. (eds.) Advances in Asset Management and Condition Monitoring. SIST, vol. 166, pp. 1195\u20131203. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-57745-2_98"},{"key":"3_CR19","doi-asserted-by":"publisher","first-page":"29857","DOI":"10.1109\/ACCESS.2020.2972859","volume":"8","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Zhang, S., Wang, B., Habetler, T.G.: Deep learning algorithms for bearing fault diagnosticsx\u2013a comprehensive review. IEEE Access 8, 29857\u201329881 (2020)","journal-title":"IEEE Access"},{"key":"3_CR20","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-3-319-50212-0_10","volume-title":"Advances in Intelligent Information Hiding and Multimedia Signal Processing","author":"W Zhang","year":"2017","unstructured":"Zhang, W., Peng, G., Li, C.: Rolling element bearings fault intelligent diagnosis based on convolutional neural networks using raw sensing signal. In: Advances in Intelligent Information Hiding and Multimedia Signal Processing. SIST, vol. 64, pp. 77\u201384. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-50212-0_10"},{"issue":"2","key":"3_CR21","doi-asserted-by":"publisher","first-page":"425","DOI":"10.3390\/s17020425","volume":"17","author":"W Zhang","year":"2017","unstructured":"Zhang, W., Peng, G., Li, C., Chen, Y., Zhang, Z.: A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 17(2), 425 (2017)","journal-title":"Sensors"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Zhao, Q., et al.: M2Det: a single-shot object detector based on multi-level feature pyramid network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9259\u20139266 (2019)","DOI":"10.1609\/aaai.v33i01.33019259"}],"container-title":["Lecture Notes in Computer Science","Intelligent Robotics and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89134-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T10:40:42Z","timestamp":1634467242000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89134-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030891336","9783030891343"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89134-3_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"18 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Robotics and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Yantai","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":"22 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icira2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icira2021.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}