**4. Experimental Verification**

In order to verify the effectiveness of the algorithm proposed in this paper, in Case One, data acquisition, fault classification, and diagnosis results are introduced in detail on the port crane built by NetCMAs. The feasibility of AF-CDN is verified. In Case Two, we verify the algorithm on the open-source rolling bearing fault dataset from Case Western Reserve University. AF-CDN can achieve excellent diagnostic results. The universality of the proposed algorithm is illustrated. The experiments in this paper were implemented on an Intel(R) Core (TM) i7-8550U CPU PC (1.80 GHz, 8 GB RAM) NVIDIA Geforce MX 150 GPU (4 GB) 64 Bit Windows 10 operating system in a Python environment.
