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InfoResearchPeer-reviewed
Handling Condition-Aware Noise and Outlier Disparities: A Transfer Learning Framework for Robust Cross-Domain Process Monitoring
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Summary
This article proposes a transfer learning framework for robust cross-domain industrial process monitoring under noise and outliers. It combines a VAE-GAN-based prelearned discriminator, an anomaly-aware dictionary learning method with an outlier matrix and L1 regularization, and a dilated convolutional neural network. Experiments on real coal mill running data and simulated cases show a minimum improvement of 26.4% and 2.6% for T² and SPE, respectively.