Industrial Transfer Learning: Boosting Machine Learning in Production In the field of production, machine learning offers great potentials to develop innovative solutions for optimization or automation. However, it faces challenges with regard to the availability of data and the high training effort of the learning models in the event of changes in the production process. In this paper, we address these challenges by introducing deep transfer learning for production. We demonstrate its potentials and benefits in a real application for predictive quality in injection molding and propose a novel approach for the continual training of neural networks across manufactured products. By creating a neural network that leverages knowledge from previous products without forgetting them, the approach shows better learning rates and more accurate predictions while requiring much less data for training. Our code is publicly available1 to reproduce our results and build upon them.

Fault detection and diagnosis based on transfer learning for multimode chemical processes Abstract   Fault detection and diagnosis (FDD) has been an active research field during the past several decades. Methods based on deep neural networks have made some important breakthroughs recently. However, networks require a large number of fault data for training. A chemical process may have several modes during production. Since fault is a low possibility event, some modes may have few fault data in history. Furthermore, collecting and annotating industrial data are extremely expensive and timeconsuming. With scarce or unlabeled fault data, networks cannot be effectively used for modeling. In this paper, we present a FDD method based on transfer learning for multimode chemical processes. To overcome the fault data rareness and no label issues in some modes, transfer learning transfers the knowledge from a source mode to a target mode for FDD. Tennessee Eastman (TE) process with five modes is utilized to verify the performance of our proposed method.