# Marine Diesel Engine Fault Detection Based on Xilinx ZYNQ SoC
## 1 Introduction  
Marine diesel engines are the primary power source for ships, driving the propellers that move the ship through water. Besides propulsion, these engines also drive generator sets to produce electricity for various electrical devices onboard, and the heat generated can be used to warm various compartments and provide hot water. The safe operation of a ship is closely linked to the reliability and safety of its main propulsion system, which plays an indispensable role during development. The reliability of any component or system refers to the ability to perform required functions under specified operating conditions over a certain period of time, and is usually expressed in terms of probability [1]. Safety factor refers to additional safety measures or margins adopted during the design or operation of a system or equipment to ensure safety, commonly used to prevent potential personal injury, loss of life, or damage to equipment and the surrounding environment, with the safety factor of a ship primarily dependent on reliability [2]. However, due to the complex nature of marine main engines and related systems, it is difficult for crew members to identify faults timely based on large amounts of data, or to predict the onset and severity of faults. The marine diesel engine, being the core power unit of a ship, plays a crucial role in its operation. A fault in the marine diesel engine can easily paralyze the ship, and if occurring during navigation, could lead to incidents such as a loss of contact or collisions, making fault detection in marine diesel engines crucial. To detect and prevent unnecessary faults at an early stage, fault diagnosis was developed. Fault diagnosis primarily involves the detection, isolation, and analysis of faults. The main purpose of fault diagnosis is to determine the type, size, timing, and location of a fault [3]. In the operation of ships, it is crucial to continuously monitor and record the technical condition and parameters of all primary and auxiliary equipment. Once these condition data are digitized, the reliability and safety of any component can be estimated, and measures to reduce the risk of failure can be taken based on these data. Additionally, equipment does not fail abruptly but shows pre-failure changes, allowing for focused attention on parameters that are prone to provide early warnings, further customizing and enhancing the maintenance plans for any component of the system. From an economic standpoint, monitoring data allows for maintenance before failures occur, extending the equipment's lifespan, which can effectively reduce repair costs and lower the lifecycle costs such as preventative costs, the costs of materials, new energy, and spare parts transportation and installation. Marine diesel engines, while ensuring safety and reliability, can also reduce unnecessary costs and enhance environmental performance. The purpose of this program is to promptly identify and report any faults in diesel engines. Compared to traditional embedded devices like Raspberry Pi and microcontrollers, the ZYNQ chip combines ARM and FPGA, offering hardware acceleration as well as the general computing capabilities of a processor. With algorithms continuously advancing, traditional embedded devices might struggle to keep up. Therefore, high-performance devices like ZYNQ will likely have an advantage in fault diagnostics moving forward. The program uses a convolutional neural network that has many advantages in classification. It can automatically extract features from the input data without the need for manually designed feature extractors. Additionally, it employs local connectivity and weight sharing, meaning each neuron is only connected to a local part of the input data. This reduces the number of parameters, lowers computational complexity, and enhances the model's robustness to variations such as rotations, scaling, and shifts. This hierarchical structure also makes the model easier to understand.