Wang, Z., Ragab, M., Yang, W., Wu, M., Jialin Pan, S., Zhang, J., & Chen, Z. (2024). Overcoming Negative Transfer by Online Selection: Distant Domain Adaptation for Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 73, 1–9. https://doi.org/10.1109/tim.2024.3472786
Abstract:
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.
At last, 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. The link for our code is public at
https://github.com/wang1351/OSAA.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Advanced Manufacturing and Engineering (AME) Programmatic Funds
Grant Reference no. : A20H6b0151
This research / project is supported by the Agency for Science, Technology and Research - Career Development Award
Grant Reference no. : C210112046
This research / project is supported by the Agency for Science, Technology and Research - Industry Alignment Fund–Pre Positioning (IAF–PP)
Grant Reference no. : M23L4a0001