**5. Conclusions**

In order to improve the fault diagnosis accuracy of rolling bearings, a novel multisensor feature fusion method is proposed in this paper. VMD is used to decompose multiple sensor signals, which reduces the redundant information contained in the raw signals. The multidomain features of each single sensor are fused at the feature-level, and the complementary information among multiple sensors is effectively utilized. The depth features of multisensor are further learned and fused with the constructed DAEN. The diagnosis effect of the proposed method is better than that of a single sensor, showing better robustness and providing a more effective means for fault signal deep mining and multisensor information fusion.

**Author Contributions:** Conceptualization, J.T.; methodology, J.T.; software, C.L.; validation, C.L.; formal analysis, H.P.; investigation, J.T. and C.L.; resources, H.P.; data curation, H.P.; writing—original draft preparation, C.L.; writing—review and editing, J.T.; visualization, H.P.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Open Project of Anhui Province Engineering Laboratory of Intelligent Demolition Equipment (No. APELIDE2021B006), the National Natural Science Foundation of China (No. 51975004).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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