**5. Conclusions**

This paper presents an adaptive fusion convolutional denoising network for the health monitoring of port crane speed gearboxes. At the same time, a Kalman filter is used to update the network parameters during the training process. Compared with traditional diagnosis methods, the main advantages of the method proposed in this paper are that the accuracy of diagnosis is improved greatly. The robustness of the diagnostic network can be significantly improved. Monitoring the status of port cranes' gearbox systems provides support for equipment health care. When the equipment is in the running-in state and subhealth state, workers can perform maintenance on bearing equipment in a timely manner so that the service time of the equipment can be effectively delayed. At the same time, once the equipment is in a sub-health state, it is necessary to monitor the system state at all times; once the system indicators reach the fault state, it is necessary to immediately shut down and perform the maintenance. This ensures the safety and reliability of the entire port machine. The results show that AF-CDN also has excellent diagnostic performance on public data sets. However, the shortcomings of this paper are that we have not integrated the other sensors well, including temperature sensors and vibration signals from other locations that the hardware system contains.

The system we have built in this paper is a four-stage bearing diagnosis system based on a single vibration sensor. In future work, we will further study the fusion of stress, temperature, and multi-directional vibration signals collected from multiple locations of the whole system so as to extract the accurate overall health status of the crane and fuse it with multi-sensor signals. This makes it possible to establish a whole health managemen<sup>t</sup> system and enables effective real-time monitoring of the full lifecycle of a port crane. Secondly,

a whole life cycle inspection system for the equipment should be established based on the multi-sensor information fusion technology described above. Meanwhile, distributed learning techniques will be focused on in order to fuse the data from the multi-location port machines. Meanwhile, distributed learning techniques will be focused on in order to fuse the data from the multi-location port machines.

**Author Contributions:** Conceptualization, R.Z.; Data curation, X.H.; Formal analysis, R.Z.; Funding acquisition, X.H.; Methodology, R.Z.; Supervision, X.H.; Writing—original draft, R.Z.; Writing— review and editing, R.Z. and X.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China gran<sup>t</sup> number 31300783.

**Institutional Review Board Statement:** No applicable.

**Informed Consent Statement:** No applicable.

**Data Availability Statement:** The original data contributions presented in the study are included in the article; further inquiries can be directed to the corresponding authors.

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