# Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System
## 2 Background
### Data Preparation  
This paper considers five failure modes within the aircraft fuel system (AFS), with their degradation severity outlined in Table 1. Each failure mode was categorised into five levels of severity, including a healthy condition and four progressively degraded states. The degradation process begins at 0% (indicating a completely healthy state) and progresses through specific intervals of degradation. The healthy and early degradation levels are considered tolerable (normal condition), while further degradation leads to functional failure (faulty condition), at which point the affected component requires repair or replacement. The boundaries between normal and faulty conditions were determined based on the impact of each failure mode on the performance metrics of static pressure and flow rate delivered to the engine (Figure 3). The red bars in the figure highlight the severity levels that significantly affect one or both performance metrics, thereby indicating faulty conditions (or functional failures). In this simplified aircraft fuel system (AFS), the five failure modes can occur independently, allowing for simultaneous occurrences of multiple (0 to 5) faults. As mentioned in Section 1, this study aims to develop an end-to-end multi-fault diagnostic algorithm without relying on ensemble methods. Specifically, the approach involves feeding a single ML algorithm with a dataset to identify all potential faults in the system. To achieve this, the work considers all possible combinations of the failure modes. Consequently, this \begin{table}
\begin{tabular}{c c c} \hline \hline
**Failure Modes** & **Normal Conditions** & **Faulty Conditions** \\ \hline Pump ext leak & 0\%, 10\%, 20\%, 30\% & 40\% \\ Pump int leak & 0\%, 10\%, 20\% & 30\%, 40\% \\ FOHE block & 0\%, 10\%, 20\% & 30\%, 40\% \\ FOHE leak & 0\%, 15\%, 30\%, 45\% & 60\% \\ Nozzle block & 0\%, 10\%, 20\% & 30\%, 40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Degradation severities of each fault. Figure 2: The Simscape model. method innovatively transforms a typical multi-label problem, which often requires multiple algorithms to be addressed, into a multiclass problem solvable by a single algorithm. These five failure modes, each with two possible states (normal and faulty), formed 32 (\(2^{5}\)) multi-fault classes, comprising one normal class and 31 faulty classes. Table 2 provides the failure modes included in each class: Class 1 represents the normal class with all components functioning correctly, while Class 32 represents the worst-case scenario, where all components experience failure. Data were collected across all 32 classes, with the simulation model run for each possible combination of degradation severity. During each run, the pump speed was set to one of five fixed values (200, 300, 400, 500, and 600 rpm) to simulate different operating conditions. The final two rows of Table 2 show the minimum number of simulation runs needed to generate sufficient data across these conditions. Each dataset comprised readings from eleven sensors: seven pressure sensors, three flow meters, and a laser sensor for measuring pump speed. Two feature selection algorithms, MRMR and tree-based methods, were applied to rank the importance of these features (Figure 4) in predicting the aforementioned 32 classes. However, the results of the two methods differed significantly. As a result, all 11 sensor outputs were selected to serve as 11 features with equal importance for training the ML algorithms. Pressure sensors were strategically placed throughout the system to capture pressure levels before and after key components, while the flow meters were positioned in series along the main pipeline: the first measured the total inflow from the fuel tank, the second monitored the pump's discharge flow downstream, and the third tracked the flow delivered to the engine. All data were normalised and rescaled to the range [0, 1] to ensure a balanced feature contribution in the ML models. Figure 3: Failure modes’ impact on the system’s behaviour.