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Outline
To address the issue of insufficient accuracy in diagnosing open-circuit faults in inverters of Permanent Magnet Synchronous Motor (PMSM) drive systems, a fault identification method integrating One-Dimensional Convolutional Neural Networks (1D-CNN) and Long Short-Term Memory (LSTM) networks is proposed. First, MATLAB/Simulink simulations are employed to model various types of open-circuit faults in three-phase inverters. Three-phase stator currents were selected as the fault feature extraction targets. The 1D-CNN-LSTM model is enhanced with the Improved Whale Optimization Algorithm (IWOA) and utilized to identify open-circuit faults in three-phase inverters. The feasibility of the algorithm is validated by comparing it with different fault diagnosis algorithms. Experimental results demonstrate that the IWOA-1D-CNN-LSTM model achieves effective fault classification with an average recognition accuracy of 98.73%.
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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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