{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:57:11Z","timestamp":1742925431424,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200984"},{"type":"electronic","value":"9783031200991"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-20099-1_37","type":"book-chapter","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:04:11Z","timestamp":1673535851000},"page":"436-446","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Domain Adversarial Interaction Network for\u00a0Cross-Domain Fault Diagnosis"],"prefix":"10.1007","author":[{"given":"Weikai","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoyi","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eng Yee","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinrong","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"issue":"14","key":"37_CR1","doi-asserted-by":"publisher","first-page":"e49","DOI":"10.1093\/bioinformatics\/btl242","volume":"22","author":"KM Borgwardt","year":"2006","unstructured":"Borgwardt, K.M., Gretton, A., Rasch, M.J., Kriegel, H.P., Sch\u00f6lkopf, B., Smola, A.J.: Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14), e49\u2013e57 (2006)","journal-title":"Bioinformatics"},{"key":"37_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.106272","volume":"133","author":"Z Chen","year":"2019","unstructured":"Chen, Z., Gryllias, K., Li, W.: Mechanical fault diagnosis using convolutional neural networks and extreme learning machine. Mech. Syst. Signal Process. 133, 106272 (2019)","journal-title":"Mech. Syst. Signal Process."},{"key":"37_CR3","unstructured":"Diederik, K., Jimmy, B., et al.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 pp. 273\u2013297 (2014)"},{"key":"37_CR4","unstructured":"Fan, H., Zhang, F., Gao, Y.: Self-supervised time series representation learning by inter-intra relational reasoning. arXiv preprint arXiv:2011.13548 (2020)"},{"key":"37_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1007\/978-3-030-47436-2_52","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"H Fan","year":"2020","unstructured":"Fan, H., Zhang, F., Wang, R., Xi, L., Li, Z.: Correlation-aware deep generative model for unsupervised anomaly detection. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12085, pp. 688\u2013700. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47436-2_52"},{"key":"37_CR6","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res 17(1), 2030\u20132096 (2016)"},{"issue":"11","key":"37_CR7","doi-asserted-by":"publisher","first-page":"5079","DOI":"10.1007\/s12206-018-1004-0","volume":"32","author":"Y-K Gu","year":"2018","unstructured":"Gu, Y.-K., Zhou, X.-Q., Yu, D.-P., Shen, Y.-J.: Fault diagnosis method of rolling bearing using principal component analysis and support vector machine. J. Mech. Sci. Technol. 32(11), 5079\u20135088 (2018). https:\/\/doi.org\/10.1007\/s12206-018-1004-0","journal-title":"J. Mech. Sci. Technol."},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Lessmeier, C., Kimotho, J.K., Zimmer, D., Sextro, W.: Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: a benchmark data set for data-driven classification. In: PHM Society European Conference, vol. 3 (2016)","DOI":"10.36001\/phme.2016.v3i1.1577"},{"key":"37_CR9","first-page":"1","volume":"70","author":"T Li","year":"2021","unstructured":"Li, T., Zhao, Z., Sun, C., Yan, R., Chen, X.: Domain adversarial graph convolutional network for fault diagnosis under variable working conditions. IEEE Trans. Instrum. Meas. 70, 1\u201310 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"37_CR10","unstructured":"Liu, M., Zeng, A., Xu, Z., Lai, Q., Xu, Q.: Time series is a special sequence: forecasting with sample convolution and interaction. arXiv preprint arXiv:2106.09305 (2021)"},{"key":"37_CR11","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., Chen, X.: 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":"37_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3118090","volume":"70","author":"ZH Liu","year":"2021","unstructured":"Liu, Z.H., Jiang, L.B., Wei, H.L., Chen, L., Li, X.H.: Optimal transport-based deep domain adaptation approach for fault diagnosis of rotating machine. IEEE Trans. Instrum. Meas. 70, 1\u201312 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"37_CR13","unstructured":"Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97\u2013105. PMLR (2015)"},{"key":"37_CR14","unstructured":"Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: 31st Proceedings of the conference on Advances in Neural Information Processing Systems (2018)"},{"key":"37_CR15","unstructured":"Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: International Conference on Machine Learning, pp. 2208\u20132217. PMLR (2017)"},{"key":"37_CR16","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"issue":"2","key":"37_CR17","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s00500-013-1055-1","volume":"18","author":"D Pandya","year":"2014","unstructured":"Pandya, D., Upadhyay, S.H., Harsha, S.P.: Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft. Comput. 18(2), 255\u2013266 (2014)","journal-title":"Soft. Comput."},{"key":"37_CR18","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: 32nd Proceedings Conference on Advances in Neural Information Processing Systems (2019)"},{"key":"37_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-319-49409-8_35","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"B Sun","year":"2016","unstructured":"Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443\u2013450. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_35"},{"issue":"1","key":"37_CR20","doi-asserted-by":"publisher","first-page":"320","DOI":"10.3390\/s20010320","volume":"20","author":"X Wang","year":"2020","unstructured":"Wang, X., Liu, F.: Triplet loss guided adversarial domain adaptation for bearing fault diagnosis. Sensors 20(1), 320 (2020)","journal-title":"Sensors"},{"issue":"8","key":"37_CR21","doi-asserted-by":"publisher","first-page":"2339","DOI":"10.3390\/s20082339","volume":"20","author":"A Yin","year":"2020","unstructured":"Yin, A., Yan, Y., Zhang, Z., Li, C., S\u00e1nchez, R.V.: Fault diagnosis of wind turbine gearbox based on the optimized lSTM neural network with cosine loss. Sensors 20(8), 2339 (2020)","journal-title":"Sensors"},{"key":"37_CR22","doi-asserted-by":"crossref","unstructured":"Zhao, Z., et al.: Applications of unsupervised deep transfer learning to intelligent fault diagnosis: a survey and comparative study. IEEE Trans. Instrum. Measur. 70 (2021)","DOI":"10.1109\/TIM.2021.3116309"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20099-1_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T02:59:54Z","timestamp":1728701994000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20099-1_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031200984","9783031200991"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20099-1_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2022\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}