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

This study proposes a novel approach to input data configuration for the fault diagnosis of three-phase induction motors. Conventional neural network (CNN)-based diagnostic methods often employ three-phase current signals and apply various image transformation techniques, such as RGB mapping, wavelet transforms, and short-time Fourier transform (STFT), to construct multi-channel input data. While such approaches outperform 1D-CNNs or grayscale-based 2D-CNNs due to their rich informational content, they require multi-channel data and involve an increased computational complexity. Accordingly, this study transforms the three-phase currents into the D-Q synchronous reference 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 a direct performance 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 using a ResNet50-based CNN. Results showed that the Id-STFT achieved the highest performance, 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 information richness and diagnostic performance, this study demonstrates that a high-performance CNN-based fault diagnosis is achievable even with grayscale representations of a single current.