Literatura académica sobre el tema "Cross-domain fault diagnosis"
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Artículos de revistas sobre el tema "Cross-domain fault diagnosis"
Wang, Xiaodong, Feng Liu y Dongdong Zhao. "Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation". Sensors 20, n.º 13 (4 de julio de 2020): 3753. http://dx.doi.org/10.3390/s20133753.
Texto completoZhang, Yongchao, Zhaohui Ren y Shihua Zhou. "A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions". Shock and Vibration 2020 (24 de julio de 2020): 1–14. http://dx.doi.org/10.1155/2020/8850976.
Texto completoMeng, Yu, Jianping Xuan, Long Xu y Jie Liu. "Dynamic Reweighted Domain Adaption for Cross-Domain Bearing Fault Diagnosis". Machines 10, n.º 4 (30 de marzo de 2022): 245. http://dx.doi.org/10.3390/machines10040245.
Texto completoChang, Hong-Chan, Ren-Ge Liu, Chen-Cheng Li y Cheng-Chien Kuo. "Fault Diagnosis of Induction Motors under Limited Data for across Loading by Residual VGG-Based Siamese Network". Applied Sciences 14, n.º 19 (4 de octubre de 2024): 8949. http://dx.doi.org/10.3390/app14198949.
Texto completoLi, Dan, Yudong Xu, Yuxun Zhou, Chao Gou y See-Kiong Ng. "Cross Domain Data Generation for Smart Building Fault Detection and Diagnosis". Mathematics 10, n.º 21 (26 de octubre de 2022): 3970. http://dx.doi.org/10.3390/math10213970.
Texto completoWang, Yuanfei, Shihao Li, Feng Jia y Jianjun Shen. "Multi-Domain Weighted Transfer Adversarial Network for the Cross-Domain Intelligent Fault Diagnosis of Bearings". Machines 10, n.º 5 (29 de abril de 2022): 326. http://dx.doi.org/10.3390/machines10050326.
Texto completoZhang, Long, Hao Zhang, Qian Xiao, Lijuan Zhao, Yanqing Hu, Haoyang Liu y Yu Qiao. "Numerical Model Driving Multi-Domain Information Transfer Method for Bearing Fault Diagnosis". Sensors 22, n.º 24 (13 de diciembre de 2022): 9759. http://dx.doi.org/10.3390/s22249759.
Texto completoJang, Gye-Bong y Sung-Bae Cho. "Cross-Domain Adaptation Using Domain Interpolation for Rotating Machinery Fault Diagnosis". IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–17. http://dx.doi.org/10.1109/tim.2022.3204093.
Texto completoShang, Qianming, Tianyao Jin y Mingsheng Chen. "A New Cross-Domain Motor Fault Diagnosis Method Based on Bimodal Inputs". Journal of Marine Science and Engineering 12, n.º 8 (1 de agosto de 2024): 1304. http://dx.doi.org/10.3390/jmse12081304.
Texto completoWang, Huaqing, Zhitao Xu, Xingwei Tong y Liuyang Song. "Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers". Sensors 23, n.º 4 (14 de febrero de 2023): 2137. http://dx.doi.org/10.3390/s23042137.
Texto completoTesis sobre el tema "Cross-domain fault diagnosis"
Ainapure, Abhijeet Narhar. "Application and Performance Enhancement of Intelligent Cross-Domain Fault Diagnosis in Rotating Machinery". University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623164772153736.
Texto completoFernandes, Montesuma Eduardo. "Multi-Source Domain Adaptation through Wasserstein Barycenters". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG045.
Texto completoMachine learning systems work under the assumption that training and test conditions are uniform, i.e., they do not change. However, this hypothesis is seldom met in practice. Hence, the system is trained with data that is no longer representative of the data it will be tested on. This case is represented by a shift in the probability measure generating the data. This scenario is known in the literature as distributional shift between two domains: a source, and a target. A straightforward generalization of this problem is when training data itself exhibit shifts on its own. In this case, one consider Multi Source Domain Adaptation (MSDA). In this context, optimal transport is an useful field of mathematics. Especially, optimal transport serves as a toolbox, for comparing and manipulating probability measures. This thesis studies the contributions of optimal transport to multi-source domain adaptation. We do so through Wasserstein barycenters, an object that defines a weighted average, in the space of probability measures, for the multiple domains in MSDA. Based on this concept, we propose: (i) a novel notion of barycenter, when the measures at hand are equipped with labels, (ii) a novel dictionary learning problem over empirical probability measures and (iii) new tools for domain adaptation through the optimal transport of Gaussian mixture models. Through our methods, we are able to improve domain adaptation performance in comparison with previous optimal transport-based methods on image, and cross-domain fault diagnosis benchmarks. Our work opens an interesting research direction, on learning the barycentric hull of probability measures
Capítulos de libros sobre el tema "Cross-domain fault diagnosis"
Lu, Weikai, Jian Chen, Hao Zheng, Haoyi Fan, Eng Yee Wei, Xinrong Cao y Deyang Zhang. "Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis". En Machine Learning for Cyber Security, 436–46. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20099-1_37.
Texto completoPing, Mingtian, Dechang Pi, Zhiwei Chen y Junlong Wang. "Cross-Domain Bearing Fault Diagnosis Method Using Hierarchical Pseudo Labels". En Neural Information Processing, 32–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8076-5_3.
Texto completoHuang, Zhe, Qing Lan, Mingxuan Li, Zhihui Wen y Wangpeng He. "A Multi-scale Feature Adaptation ConvNeXt for Cross-Domain Fault Diagnosis". En Communications in Computer and Information Science, 339–53. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7007-6_24.
Texto completoShao, Haidong, Jian Lin, Zhishan Min, Jingjie Luo y Haoxuan Dou. "Scalable Metric Meta-learning for Cross-domain Fault Diagnosis of Planetary Gearbox Using Few Samples". En Lecture Notes in Electrical Engineering, 865–72. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6901-0_89.
Texto completoZhang, Fan, Pei Lai, Qichen Wang, Tianrui Li y Weihua Zhang. "TCRNN: A Cross-domain Knowledge Transfer Acoustic Bearing Fault Diagnosis Method for Data Unbalance Issue". En Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023), 921–33. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49421-5_76.
Texto completoQin, Ruoshi y Jinsong Zhao. "Cross-domain Fault Diagnosis for Chemical Processes through Dynamic Adversarial Adaptation Network". En Computer Aided Chemical Engineering, 867–73. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-443-15274-0.50139-6.
Texto completoActas de conferencias sobre el tema "Cross-domain fault diagnosis"
Zhao, Yue, Guorong Fan, Yuxing Cao, Yong Yang, Wenhua Gao y Zengshou Dong. "A cross domain deep learning method for rolling bearing fault diagnosis". En 2024 43rd Chinese Control Conference (CCC), 4969–74. IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662230.
Texto completoChen, Zhi, Yajie Ma, Bin Jiang y Zehui Mao. "A Joint Adaptation Conditional Adversarial Network for Rolling Bearing Cross-domain Fault Diagnosis". En 2024 43rd Chinese Control Conference (CCC), 4906–11. IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661382.
Texto completoXie, Zongliang y Jinglong Chen. "Multi-Scale Attention Convolution Subdomain Adaption Network for Cross-Domain Fault Diagnosis of Machine". En 2024 Prognostics and System Health Management Conference (PHM), 153–58. IEEE, 2024. http://dx.doi.org/10.1109/phm61473.2024.00037.
Texto completoShen, Pengfei, Fengrong Bi, Daijie Tang, Xiao Yang, Meng Huang, Mingzhi Guo y Xiaoyang Bi. "Cross-Domain Fault Diagnosis of Powertrain System using Sparse Representation". En WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0420.
Texto completoLi, D., X. Nie, C. Wu, J. Song, L. Ma y J. Yang. "Bearing cross-domain fault diagnosis based on domain adversarial network". En 13th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2023). Institution of Engineering and Technology, 2023. http://dx.doi.org/10.1049/icp.2023.1698.
Texto completoYue, Fengyu y Yong Wang. "Cross-Domain Fault Diagnosis via Meta-Learning-Based Domain Generalization". En 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE, 2022. http://dx.doi.org/10.1109/case49997.2022.9926497.
Texto completoForest, Florent y Olga Fink. "Calibrated Self-Training for Cross-Domain Bearing Fault Diagnosis". En 33rd European Safety and Reliability Conference. Singapore: Research Publishing Services, 2023. http://dx.doi.org/10.3850/978-981-18-8071-1_p249-cd.
Texto completoDing, Yifei y Minping Jia. "Cross-Domain Fault Diagnosis for Rotating Machines with Multi-Scale Domain Adaptation". En 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai). IEEE, 2022. http://dx.doi.org/10.1109/phm-yantai55411.2022.9941970.
Texto completoCao, Yuxin, Yue Zhao, Lijun Li, Chenye Zhang y Zengshou Dong. "Rolling bearing cross-domain fault diagnosis based on transfer learning domain generalization". En 2023 4th International Conference on Computer Engineering and Intelligent Control (ICCEIC). IEEE, 2023. http://dx.doi.org/10.1109/icceic60201.2023.10426717.
Texto completoZhao, Chao y Weiming Shen. "An Application-oriented Perspective of Domain Generalization for Cross-Domain Fault Diagnosis". En 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2023. http://dx.doi.org/10.1109/cscwd57460.2023.10152676.
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