Efficient detection of anomalies in the cooling system of Variable Frequency Drives (VFDs) is crucial to minimize downtime costs from over-heating. Smart condition monitoring tools, especially those using machine/deep learning, have proven effective for failure detection. Recent research has focused on environmental factors like pollution and humidity affecting VFDs. Clogging is particularly harmful as it can damage pow-er electronics, leading to extended downtimes. This study explores the use of Weierstrass Generative Adversarial Networks (WGAN) for de-tecting clogging in drives, including inlet/outlet/heatsink clogging and fan blockage. WGANs are adept at recognizing complex temporal patterns due to their feedback-driven training. Despite generative AI models being typically large and unsuitable for embedded systems, this work demonstrates the feasibility of a low-parameter WGAN critic-based model for detecting cooling issues in VFDs. Using temperature signals, the model can detect clogging as low as 20-30% with high performance metrics, achieving up to 90% accuracy and an F1 score above 0.9 for heatsink clogging detection, using a lightweight 26-parameter critic model. This study shows the potential for developing low-parameter WGAN critic-based models for clogging detection in VFDs.