On the application of transfer learning in prognostics and health management

TL;DR

This paper reviews how transfer learning can enhance fault diagnostics and prognostics in industry by addressing data variability issues, providing a unified definition, and discussing application considerations and gaps.

Contribution

It offers a unified definition of transfer learning, reviews its applications in PHM, and discusses challenges and future directions for its use in industry.

Findings

Transfer learning improves model adaptability to changing conditions.

Many PHM studies successfully apply transfer learning techniques.

Identified gaps hinder widespread industrial adoption.

Abstract

Advancements in sensing and computing technologies, the development of human and computer interaction frameworks, big data storage capabilities, and the emergence of cloud storage and could computing have resulted in an abundance of data in the modern industry. This data availability has encouraged researchers and industry practitioners to rely on data-based machine learning, especially deep learning, models for fault diagnostics and prognostics more than ever. These models provide unique advantages, however, their performance is heavily dependent on the training data and how well that data represents the test data. This issue mandates fine-tuning and even training the models from scratch when there is a slight change in operating conditions or equipment. Transfer learning is an approach that can remedy this issue by keeping portions of what is learned from previous training and transferring them to the new application. In this paper, a unified definition for transfer learning and its different types is provided, Prognostics and Health Management (PHM) studies that have used transfer learning are reviewed in detail, and finally, a discussion on transfer learning application considerations and gaps is provided for improving the applicability of transfer learning in PHM.