Domain Adaptive Transfer Learning for Fault Diagnosis
This work addresses domain adaptation for fault diagnosis in industrial fleets, offering a practical solution to improve model generalization across machines, though it is incremental as it adapts existing techniques to a specific domain.
The paper tackles the problem of transferring fault diagnosis models between machines to reduce manual labeling and model modification efforts, by introducing Domain-Adversarial Neural Networks (DANN) and comparing it with existing methods in a unified experimental protocol.
Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest. Solving these domain adaptive transfer learning tasks has the potential to save large efforts on manually labeling data and modifying models for new machines in the same fleet. Although data-driven methods have shown great potential in fault diagnosis applications, their ability to generalize on new machines and new working conditions are limited because of their tendency to overfit to the training set in reality. One promising solution to this problem is to use domain adaptation techniques. It aims to improve model performance on the target new machine. Inspired by its successful implementation in computer vision, we introduced Domain-Adversarial Neural Networks (DANN) to our context, along with two other popular methods existing in previous fault diagnosis research. We then carefully justify the applicability of these methods in realistic fault diagnosis settings, and offer a unified experimental protocol for a fair comparison between domain adaptation methods for fault diagnosis problems.