# Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion
## 3 Case Study  
### Data Description  
The subject of this experiment is a Wartsila 9L34 DF marine engine. The real ship failure simulation is costly, dangerous, and destructive; therefore, the engine failure simulation performed by AVL-BOOST (R2019.2) software is used in this paper. The simulation model of the marine engine is shown in Figure 4, where SB is the system boundary; CL is the air filter; TC is the supercharger; CO is the air cooler; PL is the intake and exhaust manifold; C is the cylinder; MP is the arrangement of the detection points; and the black line is the corresponding connecting pipe. Before the fault, simulation needs to verify the effectiveness of the model. The text for the parameters under different working loads is verified for the comparison of measured values and simulation values. Error analysis and comparison results are shown in Table 1.  
As can be seen from Table 1, the error between the measured and real values of each main performance parameter under different loads is less than 4%. Especially under 100% load, the error of each main performance parameter is within 2%, and the error is within the acceptable range of requirements, so this paper chooses the model under 100% working conditions for fault simulation and fault sample collection and simulates five states, namely, injection timing advance (F1), injection timing delay (F2), supercharger efficiency decline (F3), air cooler efficiency decline (F4), and normal operation (F5). Five states are simulated, and the fault simulation scheme is shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Working** \\ **Condition** \\ \end{tabular} & **Form** & **Power/kW** & \begin{tabular}{c} **Booster Outlet** \\ **Pressure/Bar** \\ \end{tabular} & \
\begin{tabular}{c} **Air Cooler Outlet** \\ **Temperature/\({}^{\circ}\)C** \\ \end{tabular} &
\begin{tabular}{c} **Fuel** \\ **Consumption/** \\ **g/Cycle** \\ \end{tabular} \\ \hline \multirow{3}{*}{100\% load} & simulation value & 4104.99 & 4.6732 & 46.6 & 4.14 \\  & measured value & 4078 & 4.528 & 46 & 4.12 \\  & inaccuracies/\% & 0.6618 & 1.1815 & 1.3043 & 0.4854 \\ \hline \multirow{3}{*}{75\% load} & simulation value & 3393.19 & 3.2067 & 42.9 & 3.17 \\  & measured value & 3375 & 3.74 & 42 & 3.18 \\  & inaccuracies/\% & 0.5390 & \(-\)2.1471 & 2.143 & \(-\)0.3145 \\ \hline \multirow{3}{*}{50\% load} & simulation value & 2295.03 & 2.3018 & 36.7 & 2.23 \\  & measured value & 2250 & 2.57 & 38 & 2.24 \\ \cline{1-1}  & inaccuracies/\% & 2.001 & \(-\)3.4319 & \(-\)3.4211 & \(-\)0.4464 \\ \hline \multirow{3}{*}{25\% load} & simulation value & 1164.88 & 1.277 & 28.4 & 1.21 \\ \cline{1-1}  & measured value & 1125 & 1.42 & 29 & 1.26 \\ \cline{1-1}  & inaccuracies/\% & 3.5449 & \(-\)3.0282 & \(-\)2.0689 & \(-\)3.9683 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison results between model simulation data and measured data. Figure 4: Simulation model of the ship’s engine. After the above fault simulation scheme, in order to fully explore and analyze the relationship between internal engine parameters and faults and realize the diagnosis of engine faults, a total of 400 sets of samples of 23-dimensional detection index parameters were selected, of which 80 sets of samples for each state were used to carry out the fault diagnosis study. The dataset is as specific as Table 3, and the collected detection indexes are shown in Table 4.