## 5 Conclusions  
This study quantitatively evaluated the diagnostic performance of traditional three-phase (A, B, C) current-based image encoding methods versus a simplified encoding method using only the Id component derived from the D-Q synchronous reference frame. Specifically, the method fixes the Iq component at zero and uses Id as a one-dimensional time-series input to assess whether a single-axis current component can sufficiently retain fault-relevant information without relying on multi-channel data.  
Among the three-phase encoding methods, 3 Phase-RGB, which maps each phase to the R, G, and B channels, achieved a validation accuracy of 97.6% and a test accuracy of 99%, indicating strong performance. However, oscillations in validation accuracy and loss were observed during the early to mid stages of training, suggesting potential instability in the learning process. Although these effects diminished in later epochs, structural improvements are needed to enhance the model's stability. In contrast, 3 Phase-DWT consistently produced excellent results across all evaluation metrics. Conversely, 3 Phase-STFT showed a poor overall performance, likely due to suboptimal window size and step parameter settings for the raw data, indicating the need for further research and refinement.  
Among the Id-based encoding methods, Id-STFT demonstrated the highest accuracy, achieving a validation accuracy of 99.6% and a test accuracy of 99%, despite using only a single-channel input. Although grayscale encoding may be perceived as less expressive than RGB, the strong results confirm that it can serve as a fast and effective encoding method. In contrast, Id-DWT exhibited a low overall accuracy, which may be due to a partial loss of fault features during wavelet-based frequency decomposition. The Id-Linear method, despite not involving complex frequency transforms, uses a simple mapping technique to convert time-series data into a 2D image format. It still achieved a strong performance, with a validation accuracy of 99.7% and a test accuracy of 94.6%. Due to its simplicity, this method offers practical advantages for model simplification and real-time diagnostic system deployment. Future work incorporating lightweight neural network models could further expand its applicability.  
Overall, this study experimentally verified that encoding methods based on the single-axis Id current obtained through D-Q transformation can perform as well as--or better than--traditional three-phase current-based methods. These findings are attributed to the inherent advantages of the D-Q synchronous frame, including simplified signal structure, elimination of phase imbalance, and reduced sensitivity to noise--all of which positively impact CNNs' input image quality.  
Therefore, this study demonstrates the validity of an alternative encoding method for model simplification and lightweight implementation, showing that a high-performance fault diagnosis is achievable without the need for complex multi-channel inputs.  
Furthermore, while the current evaluation was conducted using data from a single testbed under controlled conditions, future work will aim to expand the dataset to include a wider range of fault types and operating scenarios. This will include load disturbances, phase imbalance, and external noise conditions, in order to further assess the generalization capability of the proposed approach in real-world environments.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.