Matches in SemOpenAlex for { <https://semopenalex.org/work/W3201753566> ?p ?o ?g. }
- W3201753566 endingPage "28" @default.
- W3201753566 startingPage "1" @default.
- W3201753566 abstract "Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep representation learning and plenty of labeled data. However, machines often operate with various working conditions or the target task has different distributions with the collected data used for training (the domain shift problem). Besides, the newly collected test data in the target domain are usually unlabeled, leading to unsupervised deep transfer learning based (UDTL-based) IFD problem. Although it has achieved huge development, a standard and open source code framework as well as a comparative study for UDTL-based IFD are not yet established. In this paper, we construct a new taxonomy and perform a comprehensive review of UDTL-based IFD according to different tasks. Comparative analysis of some typical methods and datasets reveals some open and essential issues in UDTL-based IFD which are rarely studied, including transferability of features, influence of backbones, negative transfer, physical priors, etc. To emphasize the importance and reproducibility of UDTL-based IFD, the whole test framework will be released to the research community to facilitate future research. In summary, the released framework and comparative study can serve as an extended interface and basic results to carry out new studies on UDTL-based IFD. The code framework is available at url{https://github.com/ZhaoZhibin/UDTL}." @default.
- W3201753566 created "2021-10-11" @default.
- W3201753566 creator A5011148961 @default.
- W3201753566 creator A5027894205 @default.
- W3201753566 creator A5033308608 @default.
- W3201753566 creator A5059360151 @default.
- W3201753566 creator A5077879865 @default.
- W3201753566 creator A5081020964 @default.
- W3201753566 creator A5088503073 @default.
- W3201753566 date "2021-01-01" @default.
- W3201753566 modified "2023-10-17" @default.
- W3201753566 title "Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study" @default.
- W3201753566 cites W1176839007 @default.
- W3201753566 cites W1779010541 @default.
- W3201753566 cites W1889820893 @default.
- W3201753566 cites W2010619950 @default.
- W3201753566 cites W2067802406 @default.
- W3201753566 cites W2071574349 @default.
- W3201753566 cites W2096943734 @default.
- W3201753566 cites W2107074288 @default.
- W3201753566 cites W2115403315 @default.
- W3201753566 cites W2122838776 @default.
- W3201753566 cites W2164943005 @default.
- W3201753566 cites W2194775991 @default.
- W3201753566 cites W2311607323 @default.
- W3201753566 cites W2556013418 @default.
- W3201753566 cites W2561981131 @default.
- W3201753566 cites W2584994008 @default.
- W3201753566 cites W2616881109 @default.
- W3201753566 cites W2731372149 @default.
- W3201753566 cites W2763583057 @default.
- W3201753566 cites W2765317657 @default.
- W3201753566 cites W2798149494 @default.
- W3201753566 cites W2798658180 @default.
- W3201753566 cites W2803884688 @default.
- W3201753566 cites W2805662770 @default.
- W3201753566 cites W2887782657 @default.
- W3201753566 cites W2891319189 @default.
- W3201753566 cites W2895063750 @default.
- W3201753566 cites W2896276748 @default.
- W3201753566 cites W2898375427 @default.
- W3201753566 cites W2899279252 @default.
- W3201753566 cites W2900529838 @default.
- W3201753566 cites W2900935771 @default.
- W3201753566 cites W2901639182 @default.
- W3201753566 cites W2903890850 @default.
- W3201753566 cites W2903917280 @default.
- W3201753566 cites W2904218127 @default.
- W3201753566 cites W2904737228 @default.
- W3201753566 cites W2907541186 @default.
- W3201753566 cites W2907864265 @default.
- W3201753566 cites W2907985079 @default.
- W3201753566 cites W2910540598 @default.
- W3201753566 cites W2912073957 @default.
- W3201753566 cites W2912244485 @default.
- W3201753566 cites W2912803327 @default.
- W3201753566 cites W2913854632 @default.
- W3201753566 cites W2915423430 @default.
- W3201753566 cites W2916064970 @default.
- W3201753566 cites W2919115771 @default.
- W3201753566 cites W2921803732 @default.
- W3201753566 cites W2921939179 @default.
- W3201753566 cites W2922660557 @default.
- W3201753566 cites W2923527595 @default.
- W3201753566 cites W2924922918 @default.
- W3201753566 cites W2927744156 @default.
- W3201753566 cites W2927893014 @default.
- W3201753566 cites W2930822905 @default.
- W3201753566 cites W2931331224 @default.
- W3201753566 cites W2939329110 @default.
- W3201753566 cites W2939535241 @default.
- W3201753566 cites W2945834994 @default.
- W3201753566 cites W2946048316 @default.
- W3201753566 cites W2946724317 @default.
- W3201753566 cites W2948069880 @default.
- W3201753566 cites W2948092478 @default.
- W3201753566 cites W2948429981 @default.
- W3201753566 cites W2957568672 @default.
- W3201753566 cites W2962858109 @default.
- W3201753566 cites W2963532361 @default.
- W3201753566 cites W2963693396 @default.
- W3201753566 cites W2964109570 @default.
- W3201753566 cites W2964937757 @default.
- W3201753566 cites W2969372261 @default.
- W3201753566 cites W2969736276 @default.
- W3201753566 cites W2970876095 @default.
- W3201753566 cites W2971654674 @default.
- W3201753566 cites W2981982720 @default.
- W3201753566 cites W2982100952 @default.
- W3201753566 cites W2984201918 @default.
- W3201753566 cites W2989658562 @default.
- W3201753566 cites W2990122260 @default.
- W3201753566 cites W2990239360 @default.
- W3201753566 cites W2990403609 @default.
- W3201753566 cites W2990705538 @default.
- W3201753566 cites W2991521245 @default.
- W3201753566 cites W2993397516 @default.
- W3201753566 cites W2994835796 @default.