Auswahl der wissenschaftlichen Literatur zum Thema „Cross-domain fault diagnosis“
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Zeitschriftenartikel zum Thema "Cross-domain fault diagnosis"
Wang, Xiaodong, Feng Liu und Dongdong Zhao. „Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation“. Sensors 20, Nr. 13 (04.07.2020): 3753. http://dx.doi.org/10.3390/s20133753.
Der volle Inhalt der QuelleZhang, Yongchao, Zhaohui Ren und Shihua Zhou. „A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions“. Shock and Vibration 2020 (24.07.2020): 1–14. http://dx.doi.org/10.1155/2020/8850976.
Der volle Inhalt der QuelleMeng, Yu, Jianping Xuan, Long Xu und Jie Liu. „Dynamic Reweighted Domain Adaption for Cross-Domain Bearing Fault Diagnosis“. Machines 10, Nr. 4 (30.03.2022): 245. http://dx.doi.org/10.3390/machines10040245.
Der volle Inhalt der QuelleChang, Hong-Chan, Ren-Ge Liu, Chen-Cheng Li und Cheng-Chien Kuo. „Fault Diagnosis of Induction Motors under Limited Data for across Loading by Residual VGG-Based Siamese Network“. Applied Sciences 14, Nr. 19 (04.10.2024): 8949. http://dx.doi.org/10.3390/app14198949.
Der volle Inhalt der QuelleLi, Dan, Yudong Xu, Yuxun Zhou, Chao Gou und See-Kiong Ng. „Cross Domain Data Generation for Smart Building Fault Detection and Diagnosis“. Mathematics 10, Nr. 21 (26.10.2022): 3970. http://dx.doi.org/10.3390/math10213970.
Der volle Inhalt der QuelleWang, Yuanfei, Shihao Li, Feng Jia und Jianjun Shen. „Multi-Domain Weighted Transfer Adversarial Network for the Cross-Domain Intelligent Fault Diagnosis of Bearings“. Machines 10, Nr. 5 (29.04.2022): 326. http://dx.doi.org/10.3390/machines10050326.
Der volle Inhalt der QuelleZhang, Long, Hao Zhang, Qian Xiao, Lijuan Zhao, Yanqing Hu, Haoyang Liu und Yu Qiao. „Numerical Model Driving Multi-Domain Information Transfer Method for Bearing Fault Diagnosis“. Sensors 22, Nr. 24 (13.12.2022): 9759. http://dx.doi.org/10.3390/s22249759.
Der volle Inhalt der QuelleJang, Gye-Bong, und 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.
Der volle Inhalt der QuelleShang, Qianming, Tianyao Jin und Mingsheng Chen. „A New Cross-Domain Motor Fault Diagnosis Method Based on Bimodal Inputs“. Journal of Marine Science and Engineering 12, Nr. 8 (01.08.2024): 1304. http://dx.doi.org/10.3390/jmse12081304.
Der volle Inhalt der QuelleWang, Huaqing, Zhitao Xu, Xingwei Tong und Liuyang Song. „Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers“. Sensors 23, Nr. 4 (14.02.2023): 2137. http://dx.doi.org/10.3390/s23042137.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleFernandes, Montesuma Eduardo. „Multi-Source Domain Adaptation through Wasserstein Barycenters“. Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG045.
Der volle Inhalt der QuelleMachine 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
Buchteile zum Thema "Cross-domain fault diagnosis"
Lu, Weikai, Jian Chen, Hao Zheng, Haoyi Fan, Eng Yee Wei, Xinrong Cao und Deyang Zhang. „Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis“. In Machine Learning for Cyber Security, 436–46. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20099-1_37.
Der volle Inhalt der QuellePing, Mingtian, Dechang Pi, Zhiwei Chen und Junlong Wang. „Cross-Domain Bearing Fault Diagnosis Method Using Hierarchical Pseudo Labels“. In Neural Information Processing, 32–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8076-5_3.
Der volle Inhalt der QuelleHuang, Zhe, Qing Lan, Mingxuan Li, Zhihui Wen und Wangpeng He. „A Multi-scale Feature Adaptation ConvNeXt for Cross-Domain Fault Diagnosis“. In 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.
Der volle Inhalt der QuelleShao, Haidong, Jian Lin, Zhishan Min, Jingjie Luo und Haoxuan Dou. „Scalable Metric Meta-learning for Cross-domain Fault Diagnosis of Planetary Gearbox Using Few Samples“. In Lecture Notes in Electrical Engineering, 865–72. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6901-0_89.
Der volle Inhalt der QuelleZhang, Fan, Pei Lai, Qichen Wang, Tianrui Li und Weihua Zhang. „TCRNN: A Cross-domain Knowledge Transfer Acoustic Bearing Fault Diagnosis Method for Data Unbalance Issue“. In 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.
Der volle Inhalt der QuelleQin, Ruoshi, und Jinsong Zhao. „Cross-domain Fault Diagnosis for Chemical Processes through Dynamic Adversarial Adaptation Network“. In Computer Aided Chemical Engineering, 867–73. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-443-15274-0.50139-6.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Cross-domain fault diagnosis"
Zhao, Yue, Guorong Fan, Yuxing Cao, Yong Yang, Wenhua Gao und Zengshou Dong. „A cross domain deep learning method for rolling bearing fault diagnosis“. In 2024 43rd Chinese Control Conference (CCC), 4969–74. IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662230.
Der volle Inhalt der QuelleChen, Zhi, Yajie Ma, Bin Jiang und Zehui Mao. „A Joint Adaptation Conditional Adversarial Network for Rolling Bearing Cross-domain Fault Diagnosis“. In 2024 43rd Chinese Control Conference (CCC), 4906–11. IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661382.
Der volle Inhalt der QuelleXie, Zongliang, und Jinglong Chen. „Multi-Scale Attention Convolution Subdomain Adaption Network for Cross-Domain Fault Diagnosis of Machine“. In 2024 Prognostics and System Health Management Conference (PHM), 153–58. IEEE, 2024. http://dx.doi.org/10.1109/phm61473.2024.00037.
Der volle Inhalt der QuelleShen, Pengfei, Fengrong Bi, Daijie Tang, Xiao Yang, Meng Huang, Mingzhi Guo und Xiaoyang Bi. „Cross-Domain Fault Diagnosis of Powertrain System using Sparse Representation“. In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0420.
Der volle Inhalt der QuelleLi, D., X. Nie, C. Wu, J. Song, L. Ma und J. Yang. „Bearing cross-domain fault diagnosis based on domain adversarial network“. In 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.
Der volle Inhalt der QuelleYue, Fengyu, und Yong Wang. „Cross-Domain Fault Diagnosis via Meta-Learning-Based Domain Generalization“. In 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE, 2022. http://dx.doi.org/10.1109/case49997.2022.9926497.
Der volle Inhalt der QuelleForest, Florent, und Olga Fink. „Calibrated Self-Training for Cross-Domain Bearing Fault Diagnosis“. In 33rd European Safety and Reliability Conference. Singapore: Research Publishing Services, 2023. http://dx.doi.org/10.3850/978-981-18-8071-1_p249-cd.
Der volle Inhalt der QuelleDing, Yifei, und Minping Jia. „Cross-Domain Fault Diagnosis for Rotating Machines with Multi-Scale Domain Adaptation“. In 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai). IEEE, 2022. http://dx.doi.org/10.1109/phm-yantai55411.2022.9941970.
Der volle Inhalt der QuelleCao, Yuxin, Yue Zhao, Lijun Li, Chenye Zhang und Zengshou Dong. „Rolling bearing cross-domain fault diagnosis based on transfer learning domain generalization“. In 2023 4th International Conference on Computer Engineering and Intelligent Control (ICCEIC). IEEE, 2023. http://dx.doi.org/10.1109/icceic60201.2023.10426717.
Der volle Inhalt der QuelleZhao, Chao, und Weiming Shen. „An Application-oriented Perspective of Domain Generalization for Cross-Domain Fault Diagnosis“. In 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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