Trace Data Analytics with Knowledge Distillation : DM: Big Data Management and Mining

Abstract

In this paper, we propose the “trace data analytics” for classifying fault conditions from multivariate time series sensor signals using well-known deep CNN models. In our approach, multiple sensor signals are converted into two dimensional representations using the proposed conversion methods to optimize the classification performance. Many studies on the prediction of manufacturing results using sensor signals have been conducted in the field of fault detection and classification for display and semiconductor manufacturing processes. It is challenging to apply machine learning to real-life manufacturing problems due to practical limitations, class imbalance and data insufficiency, which also make it difficult to produce a generalized model. To overcome these challenges, we propose using omni-supervised learning but with a new approach to knowledge distillation that ensembles predictions from multiple instantiations of a CNN model of synthetically generated data samples from a deep generative model. Our experiment results show that the fault classification accuracy improves substantially by applying trace data analytics to manufacturing data from display fabrication lines. The results also show that the quality of trained CNN models using the proposed knowledge distillation is maintained steadily and stably.