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您的检索式：作者名="Songjun Han"
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| 1 | Differences in changes of potential evaporation in the mountainous and oasis regions of the Tarim basin, northwest China显示文摘Data from eleven meteorological stations in the Tianshan mountains and the north slope of west Kunlun mountains, and eighteen meteorological stations in the Kaidu-Kongque river, Akesu river, Kashiger river and Yankant river oases were examined to assess the differences in changes in potential evaporation from 1960 to 2006 in the mountainous and oasis regions of the Tarim basin and the relationships of these changes to meteorological factors. The decreasing trends in potential evaporation were primarily due to the decrease in the aerodynamic terms in both the mountainous and oasis regions, but the trends in the oasis regions were more pronounced. Based on the complementary relationship between potential and actual evaporation, the decreasing trends in potential evaporation appeared to be related to the increasing trends in precipitation in the mountainous regions and the increasing trends in water consumption in the oasis regions, thus reflecting the different impacts of natural changes and anthropogenic influences. | HAN SongJun HU HePing YANG DaWen LIU QunChang | 2009 | Science China(Technological Sciences)2009，52，7： | 9 |
| 2 | Deep Residual Joint Transfer Strategy for Cross-Condition Fault Diagnosis of Rolling Bearings显示文摘Rolling bearings are key components of the drivetrain in wind turbines,and their health is critical to wind turbine operation.In practical diagnosis tasks,the vibration signal is usually interspersed with many disturbing components,and the variation of operating conditions leads to unbalanced data distribution among different conditions.Although intelligent diagnosis methods based on deep learning have been intensively studied,it is still challenging to diagnose rolling bearing faults with small amounts of samples.To address the above issue,we introduce the deep residual joint transfer strategy method for the cross-condition fault diagnosis of rolling bearings.One-dimensional vibration signals are pre-processed by overlapping feature extraction techniques to fully extract fault characteristics.The deep residual network is trained in training tasks with sufficient samples,for fault pattern classification.Subsequently,three transfer strategies are used to explore the generalizability and adaptability of the pre-trained models to the data distribution in target tasks.Among them,the feature transferability between different tasks is explored by model transfer,and it is validated that minimizing data differences of tasks through a dual-stream adaptation structure helps to enhance generalization of the models to the target tasks.In the experiments of rolling bearing faults with unbalanced data conditions,localized faults of motor bearings and planet bearings are successfully identified,and good fault classification results are achieved,which provide guidance for the cross-condition fault diagnosis of rolling bearings with small amounts of training data. | Songjun Han Zhipeng Feng | 2023 | Journal of Dynamics, Monitoring and Diagnostics2023，2，1： | 1 |
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