Two-Dimensional Color Distribution Entropy: Validation and Application on Non-Contact Fault Diagnosis for Induction Motor

Entropy-based methods are commonly used in the field of vibration signal processing and fault diagnosis due to their simplicity, ease of implementation, and strong feature extraction capabilities. Despite the successful application of 1D entropy measures in vibration signal analysis, there is a gap in 2D entropy application to images, especially for color images. Therefore, this study introduces a novel concept called two-dimensional color distribution entropy $(\text{CDEn}_{2D})$ and further develops its multiscale variant, modified multiscale two-dimensional color distribution entropy $(\text{MMCDEn}_{2D})$. This work investigates the application of $\text{MMCDEn}_{2D}$ in non-contact fault diagnosis for in-duction motors using infrared thermal imaging. The experimental results demonstrate that $\text{MMCDEn}_{2D}$ outperforms traditional two-dimensional entropy methods. Moreover, $\text{MMCDEn}_{2D}$ shows significant reliability and superiority in scenarios with limited data samples. These findings highlight its effectiveness in the field of non-contact health monitoring and fault diagnosis.