In this presentation, we explore applying deep learning to analyzing manufacturing parameter data to detect fault conditions. The manufacturing parameter data contains multivariate time series sensor signals from a fabrication line. Due to practical manufacturing limitations, datasets are often incomplete. imbalanced and/or not well-formed for deep learning models. To overcome these challenges, we apply new data augmentation methods to train a deep CNN for fault condition classification using deep generative models. We also propose an efficient method to convert multiple time series sensor inputs into a two-dimensional image representation to enable the use of image-based CNNs. Our experiment results show the fault classification accuracy improvement obtained by applying these techniques.