Handling Condition-Aware Noise and Outlier Disparities: A Transfer Learning Framework for Robust Cross-Domain Process Monitoring

Industrial process monitoring systems often perform poorly when dealing with noise (unwanted signal disturbances) and outliers (unusual data points) across different working conditions. This research proposes a transfer learning (a technique where a model trained on one task is adapted for a different but related task) framework that combines several advanced neural network approaches, including a variational autoencoder generative adversarial network (a type of AI that learns to generate and discriminate realistic data patterns) and dictionary learning (a method that finds the simplest way to represent complex data), to make monitoring systems more robust and reliable across different industrial scenarios.