# Marine Diesel Engine Fault Detection Based on Xilinx ZYNQ SoC
## 5 Conclusions  
This program utilizes the ZYNQ as the main control chip, combined with a convolutional neural network (CNN) fault detection and classification algorithm. It can identify potential fault signs and promptly alert or notify maintenance personnel for repairs, reducing the risks associated with diesel engine malfunctions. The communication end adopts the wireless LoRa network as the communication module, enabling stable communication between the ship's diesel engine and the cockpit, ensuring timely data transmission and processing. In summary, this program can effectively enhance the safety of ship's diesel engines, providing reliable support and protection for maritime navigation. Furthermore, the test results obtained from the booster diesel engine simulation software align with expectations.  
In the maritime industry, most researchers use traditional embedded devices such as Raspberry Pi and microcontrollers for algorithmic computations. ZYNQ research is generally used in high-performance areas such as image recognition. However, the maritime industry also requires high-performance equipment for technological advancement. While algorithms need to keep pace with the times, they also require sufficiently powerful carriers to enable a greater intelligence and automation of facilities on board ships. This paper aims to explore the possibility of combining ZYNQ with ships, validate it, and ultimately discover its feasibility. However, the learning curve of ZYNQ is much higher than that of traditional embedded devices, and the economic cost is also higher. Yet, these costs bring about superior performance, coupled with the advantages of FPGA integration. As a product after 2010, ZYNQ is an emerging device for the maritime industry, which is also urgently needed for the development of intelligent unmanned ships. Both equipment and technology require further development. Thus, the original intention of this paper is to promote the integration of emerging chips with ships.  
Conceptualization, H.W. and R.J.; funding acquisition, R.J.; methodology, H.W.; software, H.W.; validation, X.W., X.C. and T.L.; formal analysis, X.C.; investigation, X.W.; resources, H.W.; data curation, H.W.; writing--original draft preparation, H.W.; writing--review and editing, H.W.; visualization, H.W.; supervision, R.J.; project administration, T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Innovation Project of the Offshore LNG Equipment Industry Chain (CBG3N21-2-7), by Development of Ship Operation Condition Monitoring and Simulation Platform, Liaoning Provincial Department of Natural Resources (No. 1638882993269) and by Development of liquid cargo and electromechanical simulation operation system for LNG ship, Ministry of Industry and Information Technology of the People's Republic China (CBG3N21-3-3).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors extend their appreciation to the anonymous reviewers for their valuable feedback.  
The authors declare no conflicts of interest.