# Marine Diesel Engine Fault Detection Based on Xilinx ZYNQ SoC
## 1 Introduction
Convolutional neural networks can also mitigate risks like overfitting, commonly encountered in algorithms, through techniques such as parameter sharing and regularization. Marine diesel engines are monitored using many sensors, which means a large volume of sensor data needs to be transmitted to a supervisory computer for data analysis and processing. After the analysis, the results are used for fault diagnosis and a health assessment of the ship's equipment. Currently, as the development of ship intelligence increasingly leans towards unmanned operations, analyzing data via supervisory computers inevitably faces two problems. First, with numerous devices in the ship's engine room, uploading large amounts of data simultaneously can cause data confusion, data loss, and insufficient bandwidth to support the volume of data. Second, the process of the supervisory computer sending commands to the corresponding devices is complex and lacks feedback, which is a significant issue in communication. This paper, after studying factors affecting marine diesel engine failures, proposes a multi-protocol aggregation message transmission protocol. It facilitates the connection of sensors using different protocols with the ZYNQ PS side and communication with the supervisory computer. Additionally, through using open-source data to simulate the operation data of the marine diesel engine during navigation, the ZYNQ PL side's convolutional neural network is used for fault diagnosis. Finally, communication between the LoRa module and the PC, as well as command transmission from the PC to the ZYNQ, is achieved. Researchers both domestically and internationally have made significant progress in the field of fault detection. For instance, [PERSON] and [PERSON] employed generative adversarial networks (GANs) for monitoring ship machinery equipment, achieving excellent results when applied to real ships, with data processing conducted on upper computers [4]. [PERSON] and [PERSON] utilized convolutional neural networks (CNNs) to achieve over 95% accuracy in monitoring gearbox conditions, demonstrating the capability of CNNs in fault diagnosis [5]. Additionally, [PERSON], [PERSON], and others utilized Convolutional Autoencoders for predictive maintenance of rolling bearings, exhibiting impressive performance [6]. [PERSON], [PERSON], and their colleagues combined second-order convolutional neural networks (QCNN) with audio and vibration signals from bearings, indicating the improved CNNs' ability to diagnose complex parameters such as vibration signals [7]. [PERSON], [PERSON], and others achieved a diagnostic accuracy of over 90% for fuel injection systems using neural network (NN) machine learning methods for amplitude and frequency analysis [8]. [PERSON], [PERSON], and their team established an improved BAS algorithm on FPGA and verified its feasibility through hardware simulation, demonstrating FPGA's capability for algorithmic computations [9]. [PERSON], [PERSON], and their collaborators used a multi-statistical parameter fault detection method for monitoring weak characteristic faults in diesel engines, showing that even engines with weak characteristics can be diagnosed using algorithms [10]. [PERSON], [PERSON], and others conducted target detection using convolutional neural networks on System-on-Chip (SOC), providing significant reference value for implementing CNN calculations on ZYNQ in this paper [11]. Lastly, [PERSON], [PERSON], and their team completed defogging tasks using ZYNQ, indicating its feasibility for algorithmic computations and real-time calculations [12]. Collectively, these papers provide valuable references for this paper and contribute to the design of this program.