Full
Outline
Compared with the rigid support scheme of traditional ship shafting systems, the bearing load in an elastically supported shafting system is more easily affected by external disturbances. To address the difficulty in accurately obtaining the bearing load under different operating conditions after the ship is launched, this paper proposes a method for predicting the bearing load of elastically supported shafting systems based on transfer learning. This method focuses on the influence of vertical disturbance forces caused by pressure changes in the air spring isolators on the shafting system. It collects strain data from characteristic regions and air spring pressure data during the installation and commissioning phase of the shafting system. These data, together with finite element simulation data, are used to build a bearing load prediction model with a GA-BP neural network algorithm. Validation with measured data shows that this method has good generalization ability and prediction accuracy for untrained operating conditions, with an average error of 8.8%.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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