  Unsupervised Domain Adaptation (UDA) has emerged as a key solution in
data-driven fault diagnosis, addressing domain shift where models underperform
in changing environments. However, under the realm of continually changing
environments, UDA tends to underperform on previously seen domains when
adapting to new ones - a problem known as catastrophic forgetting. To address
this limitation, we introduce the EverAdapt framework, specifically designed
for continuous model adaptation in dynamic environments. Central to EverAdapt
is a novel Continual Batch Normalization (CBN), which leverages source domain
statistics as a reference point to standardize feature representations across
domains. EverAdapt not only retains statistical information from previous
domains but also adapts effectively to new scenarios. Complementing CBN, we
design a class-conditional domain alignment module for effective integration of
target domains, and a Sample-efficient Replay strategy to reinforce memory
retention. Experiments on real-world datasets demonstrate EverAdapt superiority
in maintaining robust fault diagnosis in dynamic environments. Our code is
available: https://github.com/mohamedr002/EverAdapt
