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- Title
Three-Phase Inverter Fault Diagnosis Based on an Improved Deep Residual Network.
- Authors
Fu, Yanfang; Ji, Yu; Meng, Gong; Chen, Wei; Bai, Xiaojun
- Abstract
This study addresses the challenges of limited fault samples, noise interference, and low accuracy in existing fault diagnosis methods for three-phase inverters under real acquisition conditions. To increase the number of samples, Wavelet Packet Decomposition (WPD) denoising and a Conditional Variational Auto-Encoder (CVAE) are used for sample enhancement based on the existing faulty samples. The resulting dataset is then normalized, pre-processed, and used to train an improved deep residual network (SE-ResNet18) fault diagnosis model with a channel attention mechanism. Results show that the augmented fault samples improve the diagnosis accuracy compared with the original samples. Furthermore, the SE-ResNet18 model achieves higher fault diagnosis accuracy with fewer iterations and faster convergence, indicating its effectiveness in accurately diagnosing inverter open-circuit faults across various sample situations.
- Subjects
FAULT diagnosis; DIAGNOSIS methods
- Publication
Electronics (2079-9292), 2023, Vol 12, Issue 16, p3460
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics12163460