Full
Outline
To address the issue of poor accuracy and low efficiency in diagnosing inter turn short circuit faults of motors in new energy vehicles under variable operating conditions due to lack of data, this paper proposes a fault diagnosis method based on Attention Mechanism Optimized Multi-Scale Convolutional Neural Networks (Attention-MCNN) combined with transfer learning. Firstly, an inter- turn short circuit experimental platform for permanent magnet synchronous motors is established to collect raw three-phase current data, and construct a labeled dataset. Secondly, the Attention-MCNN network structure and parameters are determined through ablation experiments, and the network is trained to obtain a pre-trained network model. Finally, transfer learning is introduced to freeze the pre-trained model and fine tune the network by inputting labeled data under other operating conditions. A network model with the ability to recognize the characteristics of target domain data is obtained to achieve the diagnosis of inter turn short circuit faults in new energy vehicle motors. The experimental results show that the proposed method has an average fault diagnosis accuracy of 95.67%, with a detection time of about 5 s, demonstrating lower data dependency compared with traditional methods.
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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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