Strategic Data Augmentation with CTGAN for Smart Manufacturing:
  Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper
  Production
Hamed Khosravi Sarah Farhadpour Manikanta Grandhi Ahmed Shoyeb Raihan Srinjoy Das Imtiaz Ahmed
A significant challenge for predictive maintenance in the pulp-and-paper
industry is the infrequency of paper breaks during the production process. In
this article, operational data is analyzed from a paper manufacturing machine
in which paper breaks are relatively rare but have a high economic impact.
Utilizing a dataset comprising 18,398 instances derived from a quality
assurance protocol, we address the scarcity of break events (124 cases) that
pose a challenge for machine learning predictive models. With the help of
Conditional Generative Adversarial Networks (CTGAN) and Synthetic Minority
Oversampling Technique (SMOTE), we implement a novel data augmentation
framework. This method ensures that the synthetic data mirrors the distribution
of the real operational data but also seeks to enhance the performance metrics
of predictive modeling. Before and after the data augmentation, we evaluate
three different machine learning algorithms-Decision Trees (DT), Random Forest
(RF), and Logistic Regression (LR). Utilizing the CTGAN-enhanced dataset, our
study achieved significant improvements in predictive maintenance performance
metrics. The efficacy of CTGAN in addressing data scarcity was evident, with
the models' detection of machine breaks (Class 1) improving by over 30% for
Decision Trees, 20% for Random Forest, and nearly 90% for Logistic Regression.
With this methodological advancement, this study contributes to industrial
quality control and maintenance scheduling by addressing rare event prediction
in manufacturing processes.