Domain Adaptive Transfer Learning for Fault Diagnosis

TL;DR

This paper explores domain adaptation techniques, especially Domain-Adversarial Neural Networks, to improve fault diagnosis models' ability to generalize across different machines and conditions, reducing manual labeling efforts.

Contribution

It introduces and evaluates domain adaptation methods, including DANN, for fault diagnosis, providing a unified experimental protocol for fair comparison in realistic settings.

Findings

DANN improves fault diagnosis accuracy on new machines.

Domain adaptation methods outperform traditional models without adaptation.

A standardized protocol enables fair evaluation of adaptation techniques.

Abstract

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.