  One of the most significant obstacles in bearing fault diagnosis is a lack of
labeled data for various fault types. Also, sensor-acquired data frequently
lack labels and have a large amount of missing data. This paper tackles these
issues by presenting the PTPAI method, which uses a physics-informed deep
learning-based technique to generate synthetic labeled data. Labeled synthetic
data makes up the source domain, whereas unlabeled data with missing data is
present in the target domain. Consequently, imbalanced class problems and
partial-set fault diagnosis hurdles emerge. To address these challenges, the
RF-Mixup approach is used to handle imbalanced classes. As domain adaptation
strategies, the MK-MMSD and CDAN are employed to mitigate the disparity in
distribution between synthetic and actual data. Furthermore, the partial-set
challenge is tackled by applying weighting methods at the class and instance
levels. Experimental outcomes on the CWRU and JNU datasets indicate that the
proposed approach effectively addresses these problems.
