From Raw to Ready: Industrial Fault Data Enhancement Via Preprocessing and Balancing

In recent years, predictive maintenance has emerged as a critical component for improving the efficiency and reliability of industrial systems. However, much of the existing research has primarily emphasized model development, often overlooking the fundamental role of data quality and class distribution in shaping predictive performance. To address this gap, this study proposes an integrated preprocessing framework that ensures high-quality data readiness across all stages. A case study was conducted on a dataset of industrial sensors for fault prediction. The preprocessing pipeline involved handling missing values using K-Nearest Neighbors (KNN), detecting outliers with Isolation Forest (IF), and correcting abnormal values through the Clipping method. To address data imbalance, synthetic data were generated using Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), and a hybrid GAN-VAE model that leverages the strengths of both approaches. The hybrid GAN-VAE demonstrated superior data generation performance, yielding the highest Pearson correlation and best Kernel Density Estimation (KDE) fit, thereby ensuring dataset reliability for training. The effectiveness of the preprocessing framework was validated using a 1-Dimensional Convolutional Neural Network (1D-CNN) classifier, which achieved a high accuracy of 98.83%.