Early fault detection and performance assessment are critical components of machine health management, with the primary goals being the detection of incipient faults and the development of a health index (HI) to monitor degradation. However, current methods typically require significant domain expertise or labeled faulty data, and often face challenges in simultaneously achieving accurate early anomaly detection and maintaining a clear performance degradation trend. To address these limitations, in this work, a simple unsupervised fault detection framework is stated for early fault detection and performance assessment. The proposed method is grounded in spectrum analysis, where the logarithmic envelope spectrum (logES) is first introduced to enhance fault signatures and ensure a consistent representation of degradation. Subsequently, a variational autoencoder (VAE) is employed for anomaly detection, using the logES as input. The VAE learns the spectral distribution of healthy operational data, and its reconstruction error is used as the HI. This HI not only achieves precise early fault detection but also exhibits strong monotonicity and robustness in tracking performance degradation over time. Extensive experiments on four bearing run-to-failure datasets validate that the proposed framework delivers highly accurate incipient fault detection and generates a reliable HI. Compared to other established HI methods, the proposed framework accurately characterizes the health status, establishing itself as a powerful tool for machine health management without the necessity for labeled fault data.

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.