  Extensive research has been conducted on fault diagnosis of planetary
gearboxes using vibration signals and deep learning (DL) approaches. However,
DL-based methods are susceptible to the domain shift problem caused by varying
operating conditions of the gearbox. Although domain adaptation and data
synthesis methods have been proposed to overcome such domain shifts, they are
often not directly applicable in real-world situations where only healthy data
is available in the target domain. To tackle the challenge of extreme domain
shift scenarios where only healthy data is available in the target domain, this
paper proposes two novel domain knowledge-informed data synthesis methods
utilizing the health data map (HDMap). The two proposed approaches are referred
to as scaled CutPaste and FaultPaste. The HDMap is used to physically represent
the vibration signal of the planetary gearbox as an image-like matrix, allowing
for visualization of fault-related features. CutPaste and FaultPaste are then
applied to generate faulty samples based on the healthy data in the target
domain, using domain knowledge and fault signatures extracted from the source
domain, respectively. In addition to generating realistic faults, the proposed
methods introduce scaling of fault signatures for controlled synthesis of
faults with various severity levels. A case study is conducted on a planetary
gearbox testbed to evaluate the proposed approaches. The results show that the
proposed methods are capable of accurately diagnosing faults, even in cases of
extreme domain shift, and can estimate the severity of faults that have not
been previously observed in the target domain.
