Full
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%.
89
9
| 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 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
