  New operating conditions can result in a significant performance drop of
fault diagnostics models due to the domain shift between the training and the
testing data distributions. While several domain adaptation approaches have
been proposed to overcome such domain shifts, their application is limited if
the fault classes represented in the two domains are not the same. To enable a
better transferability of the trained models between two different domains,
particularly in setups where only the healthy data class is shared between the
two domains, we propose a new framework for Partial and Open-Partial domain
adaptation based on generating distinct fault signatures with a Wasserstein
GAN. The main contribution of the proposed framework is the controlled
synthetic fault data generation with two main distinct characteristics.
Firstly, the proposed methodology enables to generate unobserved fault types in
the target domain by having only access to the healthy samples in the target
domain and faulty samples in the source domain. Secondly, the fault generation
can be controlled to precisely generate distinct fault types and fault severity
levels. The proposed method is especially suited in extreme domain adaption
settings that are particularly relevant in the context of complex and
safety-critical systems, where only one class is shared between the two
domains. We evaluate the proposed framework on Partial as well as Open-Partial
domain adaptation tasks on two bearing fault diagnostics case studies. Our
experiments conducted in different label space settings showcase the
versatility of the proposed framework. The proposed methodology provided
superior results compared to other methods given large domain gaps.
