  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.
