  Unsupervised domain adaptation (UDA) has achieved remarkable success in fault
diagnosis, bringing significant benefits to diverse industrial applications.
While most UDA methods focus on cross-working condition scenarios where the
source and target domains are notably similar, real-world applications often
grapple with severe domain shifts. We coin the term `distant domain adaptation
problem' to describe the challenge of adapting from a labeled source domain to
a significantly disparate unlabeled target domain. This problem exhibits the
risk of negative transfer, where extraneous knowledge from the source domain
adversely affects the target domain performance. Unfortunately, conventional
UDA methods often falter in mitigating this negative transfer, leading to
suboptimal performance. In response to this challenge, we propose a novel
Online Selective Adversarial Alignment (OSAA) approach. Central to OSAA is its
ability to dynamically identify and exclude distant source samples via an
online gradient masking approach, focusing primarily on source samples that
closely resemble the target samples. Furthermore, recognizing the inherent
complexities in bridging the source and target domains, we construct an
intermediate domain to act as a transitional domain and ease the adaptation
process. Lastly, we develop a class-conditional adversarial adaptation to
address the label distribution disparities while learning domain invariant
representation to account for potential label distribution disparities between
the domains. Through detailed experiments and ablation studies on two
real-world datasets, we validate the superior performance of the OSAA method
over state-of-the-art methods, underscoring its significant utility in
practical scenarios with severe domain shifts.
