CNN Input Data Configuration Method for Fault Diagnosis of Three-Phase Induction Motors Based on D-Axis Current in the D-Q Synchronous Reference Frame

This study proposes a novel approach to input data configuration for fault diagnosis of three-phase induction motors. Conventional neural network (CNN)-based diagnostic methods often employ three-phase current signals and apply various image transfor-mation techniques, such as RGB-mapping, wavelet transforms, and short-time Fourier transform (STFT), to construct multi-channel input data. While such approaches are out-perform 1D-CNNs or grayscale-based 2D-CNNs due to their rich informational content, they require multi-channel data and involve increased computational complexity. Ac-cordingly, this study transforms the three-phase currents into the D-Q synchronous refer-ence frame, and utilizes the D-axis current (Id) for image transformation. The Id is used to generate input data using the same image processing techniques, allowing for direct per-formance comparison under identical CNN architectures. Experiments were conducted under consistent conditions using both three-phase-based and Id-based methods, each applied to RGB-mapping, DWT, and STFT. The classification accuracy was evaluated us-ing a ResNet50-based CNN. Results showed that the Id-STFT achieved the highest per-formance, with a validation-accuracy of 99.6% and a test-accuracy of 99.0%. While the RGB representation of three-phase signals has traditionally been favored for its infor-mation richness and diagnostic performance, this study demonstrates that high-performance CNN-based fault diagnosis is achievable even with grayscale repre-sentations of a single current.