Shahid Beheshti University International Journal of Research and Technology in Electrical Industry 2821-0190 4 2 2025 07 01 Transfer Learning-Based Open-Circuit Fault Detection Using Time-Frequency Analysis on Small Datasets for Voltage Source Inverters. 106599 10.48308/ijrtei.2025.238719.1074 EN Abbas Babaei Birag Department of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran Ahmad Salemnia Department of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran Nazanin Pourmoradi Department of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran Journal Article 2025 02 09 Open-circuit (OC) fault identification in Voltage-Source Inverters (VSIs) is a critical challenge for the reliability of power systems and motor drives. While Deep Learning (DL) technologies offer automatic feature extraction, they typically suffer from high computational costs and the requirement for massive labeled datasets, which are scarce in real-world industrial scenarios. This paper provides an innovative lightweight diagnostic approach utilizing Transfer Learning (TL) to address current issues of data scarcity and training inefficiency. The pre-trained SqueezeNet model, an efficient Convolutional Neural Network (CNN) with a lower parameter count, is fine-tuned to properly categorize various fault states. In the proposed methodology, the three-phase output current signals are first converted into time-frequency scalograms using the Continuous Wavelet Transform (CWT) to capture rich transient fault features. Subsequently, these visual representations are processed by the network. The proposed method achieves 99.90% accuracy on a small dataset (2000 samples) with considerably reduced training time relative to training deep models from scratch. These findings demonstrate the effectiveness and robustness of the suggested methodology for real-time fault diagnosis in inverters.