The recent developments in the research area of machine learning models for tabular data and fault diagnosis in industrial machinery have shown significant advancements. Notably, there is a shift towards hybrid models that combine the strengths of traditional methods with neural network architectures, enhancing both performance and interpretability. For instance, models are now integrating learnable activation functions within random feature models, significantly boosting expressivity and interpretability. In the domain of fault diagnosis, vision transformers are being employed to handle noisy environments effectively, with attention mechanisms improving feature extraction and classification accuracy. Additionally, explainable models using shallow network architectures are being developed to provide interpretable results, which is crucial for real-time monitoring and scientific research. These innovations are paving the way for more robust, scalable, and interpretable machine learning solutions in industrial applications.