A Novel Transfer Learning Strategy Based on Inception-ResNet+ 1D-CNN for DC–DC Converter Degradation Fault Diagnosis

Reliable fault diagnosis of DC-DC converters is critical for ensuring the safety and operational continuity of electric vehicles (EVs) and industrial power systems. In practical applications, early detection of progressive degradation can prevent catastrophic failures and significantly reduce maintenance costs. However, developing robust diagnostic models is hindered by sensor noise, environmental disturbances, and the scarcity of labeled fault data—particularly for subtle degradation scenarios where real-world collection is expensive. To address these issues, this paper proposes a transfer learning framework based on Inception-Residual Neural Network (ResNet) + One-Dimensional Convolutional Neural Network (1D-CNN), aiming to enhance diagnostic performance under small-sample conditions. The proposed network extracts discriminative features from simulated voltage signals (source domain) and transfers this knowledge to real-world experimental data (target domain). To reduce domain discrepancies, a Whitening and Coloring Covariance Alignment (WCA) is employed for global feature alignment, while a novel Classwise WCA strategy further refines the alignment at the category level to preserve fault-specific structures. Additionally, the classifier is fine-tuned using L2 Starting Point (L2-SP) regularization, which constrains parameter shift to prevent overfitting under limited supervision. The method is non-intrusive and practical, operating efficiently using only the output voltage ripple. Experimental results validate the effectiveness of the proposed approach, demonstrating a diagnostic accuracy of 91.6% even with limited samples, significantly outperforming conventional transfer learning methods in cross-domain fault identification tasks.