Data-Hungry Fault Detection Algorithms Can Try Transfer Learning for Starters

Transfer learning is a standard technique for improving deep learning model performance, in particular when training data is limited. The basic idea is to use the weights learned from an original dataset and transfer these weights to a different (target) dataset. Using an existing state-of-the art method for fault detection, we implemented transfer learning between data sources, conducting extensive experimentation to compare seven fault detection datasets, spanning univariate and multivariate cases. We cross-compared models pre-trained on one dataset and then transferred to other datasets. Our results indicate that, compared to the other datasets, XJTU generalises well in multiple cases when used for pre-training.