Transfer learning based open‑circuit fault diagnosis method for three‑phase inverters

Qinqin Chai; Haodong Li; Wu Wang; Qibin Yan

Transfer learning based open‑circuit fault diagnosis method for three‑phase inverters

Vol. 25, No. 6, pp. 1030-1040, Jun. 2025

10.1007/s43236-024-00958-3

domain adaptation

Fault diagnosis

One-dimensional convolutional neural networks

Pseudo-label

Threephase inverter

Transfer Learning

Abstract

Fault diagnosis of the power devices in inverters is crucial for improving equipment reliability. However, the signal fluctuations caused by load variations during actual operation pose new challenges for inverter fault diagnosis. Existing data-driven fault diagnosis methods are designed based on specific system fault databases, making it difficult to overcome the influence of system parameter changes. In addition, existing transfer learning methods for variable working conditions often require a large amount of unlabeled target domain data for model training. In addition, the application is limited by the sample size of the new working conditions. To tackle these challenges, this paper presents a novel approach for diagnosing open-circuit faults in three-phase inverters by leveraging transfer learning. In this approach, the output voltage of different three-phase inverter loads is used as the fault signal. Then a one-dimensional convolutional neural network integrating attention mechanisms and global average pooling layers is introduced to effectively capture the channel and spatial features of fault characteristics. Next, a domain adversarial neural network is employed to enable the diagnostic model to learn domain-invariant features, so that the target domain and source domain cannot be distinguished. Thus, the model built on the source domain can adapt to changing working conditions. Finally, by utilizing an iterative pseudo-labeling method to train the model, high-precision diagnostic outcomes are achieved and a limited number of labeled samples from the target domain are needed. Experimental results show that the proposed method achieves an average diagnostic accuracy of 96.63% in transfer diagnosis tasks across different systems, and exhibits robustness in environments with various types of noise.

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Cite this article

[IEEE Style]

Q. Chai, H. Li, W. Wang, Q. Yan, "Transfer learning based open‑circuit fault diagnosis method for three‑phase inverters," Journal of Power Electronics, vol. 25, no. 6, pp. 1030-1040, 2025. DOI: 10.1007/s43236-024-00958-3.

[ACM Style]

Qinqin Chai, Haodong Li, Wu Wang, and Qibin Yan. 2025. Transfer learning based open‑circuit fault diagnosis method for three‑phase inverters. Journal of Power Electronics, 25, 6, (2025), 1030-1040. DOI: 10.1007/s43236-024-00958-3.