Noncontact Fault Diagnosis of Electrical Equipment Using Modified Multiscale Two‐Dimensional Color Distribution Entropy and Thermal Imaging

Effective health monitoring of electrical equipment is critical for industrial reliability. Although infrared thermal imaging offers a powerful noncontact diagnostic method, accurately interpreting its complex and often noisy thermal patterns remains a significant challenge. Entropy‐based analysis is well suited for quantifying this complexity, but its application to images has been limited. Existing two‐dimensional entropy methods are not only less developed than their one‐dimensional counterparts but also typically require converting thermal images to grayscale, which discards vital diagnostic information from color channels. To overcome these limitations, this study introduces the modified multiscale two‐dimensional color distribution entropy (MMCDEn 2D ). This novel method directly integrates the attributes of the RGB, preserving a richer feature set for analysis. The effectiveness of the proposed method is demonstrated first through synthetic signals, showing low sensitivity to image size and high computational efficiency. The study further extends the application of entropy‐based analysis to noncontact health monitoring scenarios, implementing MMCDEn 2D for thermal image‐based fault diagnosis of induction motors and power transformers. The method achieves a diagnostic accuracy that exceeds 95%, significantly outperforming traditional approaches. Crucially, it demonstrates superior robustness in challenging scenarios, improving accuracy by 2%–5% under high‐noise conditions and with small sample sizes. These results establish MMCDEn 2D as a highly effective and reliable tool to advance noncontact fault diagnosis in critical electrical equipment.