## 2 Related Works
### RGB Mapping-Based Approach  
Mapping three-phase current signals directly into the three channels of an RGB image is one of the most intuitive and straightforward methods for CNN input configuration.  
[PERSON] et al. mapped three sensor signals to RGB channels to form RGB images, which were then fed into a CNN model for bearing fault diagnosis. They reported that the RGB-based approach significantly outperformed the grayscale-based method in terms of diagnostic performance [7].  
[PERSON] proposed three methods for classifying the operating conditions of rotating machinery using CNNs: grayscale, RGB by Type, and RGB by Axis. These methods involved converting sensor time-series data into grayscale and RGB images. In particular, the RGB by Axis approach mapped X, Y, and Z sensor axis data to R, G, and B channels respectively, enabling the CNN to learn inter-axis interactions visually. This approach demonstrated superior performance [8].  
[PERSON] et al. further constructed 16 \(\times\) 16 \(\times\) 3 RGB images using three-phase current signals for CNN-based fault detection and localization. They emphasized the advantage of RGB representation in preserving phase and amplitude relationships [9].  
[PERSON] et al. proposed an image transformation technique based on a multidimensional distance matrix (MDM) to preserve the temporal correlation between multisensor signals, along with a multi-scale adaptive feature fusion CNN (MAFFCNN) structure to effectively integrate features extracted from various CNN layers. They pointed out that traditional RGB image transformation and single-input CNN structures may suffer from information loss and noise sensitivity, and they validated their enhanced diagnostic framework'sperformance and robustness through the simultaneous consideration of time-series and positional features [10].  
[PERSON] and [PERSON] transformed 1D vibration signals into 2D grayscale images for bearing fault diagnosis. They then performed a connected component analysis based on regions of interest (ROIs) to generate RGB vibration images (RGBVIs) containing both texture and color information. These RGBVIs were input into a CNN in a two-stage structure to automatically learn features and classify fault states, achieving a high accuracy and robustness under diverse operating conditions [11].  
[PERSON] et al. (2025) visualized time-domain, frequency-domain, and time-frequency-domain information into the R, G, and B channels, respectively, to create RGB images. These were then used as input for a transfer learning-based ConvNeXt model (TL-CoCNN) for rotating machinery fault diagnosis. This method amplified fault-related features through image-based visualization and achieved an outstanding classification performance even on small datasets by leveraging pretrained large models. It also demonstrated an improved diagnostic accuracy and generalizability while minimizing feature loss [12].  
In summary, the RGB mapping approach offers relatively simple data processing and enables CNN models to intuitively learn image patterns. However, it also presents limitations such as sensitivity to signal noise and phase imbalance and increased computational complexity.