**5. Conclusions**

This study focuses on cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data. Infrared thermal images and vibration signals are fused to characterize the health states of the gearbox, which can effectively recognize structural and non-structural faults. Moreover, the domain-adaptation neural network is trained via adversarial training using fusion data samples to extract the common transfer knowledge, called the FDACNN method. By performing this, the proposed FDACNN method can be used to recognize the unlabeled target domain samples of gearbox. For validation, the proposed FDACNN method is used to analyze gearbox multi-source heterogeneous data measured under various operating conditions. Moreover, we compare the FDACNN method with four other relevant methods to confirm its superiority in cross-domain fault diagnosis of gearboxes under various operating conditions. The results demonstrate that the proposed method obtains highest classification accuracy among four methods.

**Author Contributions:** Data curation, Z.Z.; Methodology, Y.L.; Writing—original draft, G.M.; Writing—review & editing, B.Q. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research is supported by National Natural Science Foundation of China under Grant 51805434 and 12172290 and Key Laboratory of Equipment Research Foundation under Grant 6142003190208.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare that they have no conflict of interest.
