# Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System
## 3 Methodology
### Interpretability of the Trained Diagnostic Algorithms
In Figure 15, the blue and orange bars indicate the overlap between the outputs of LIME and SHAP and the engineering insights, representing the interpretability of these opaque algorithms. The grey bars depict the F-1 score of the ML algorithms on the validation dataset, effectively decomposing the overall F-1 score from Table 5 across individual classes. For instance, for Class 2 in the ANN which related to a clogged nozzle prediction, the F-1 score--a measure of predictive accuracy--approached 95%. However, only two and one of the four most sensitive features identified by LIME and SHAP aligned with engineering insights. Although the ANN generally performed well across classes, the interpretability provided by the XAI techniques varied significantly. The table in Figure 15 summarises the number of classes at each level of overlap. Compared to the SVM (Figure 15b), the ANN (Figure 15a) had more classes with high interpretability, defined as an overlap of 75% or greater. Based on these tables, LIME provided slightly better interpretability than SHAP for the ANN and the SVM, achieving 61% and 52% overlap, respectively. Thus, among the combinations of XAI techniques and opaque algorithms in this study, LIME provided the highest interpretability with the ANN, while SHAP offered the lowest interpretability with the SVM. LIME's results for the ANN and the SVM in Figure 15 exhibit an approximate bimodal pattern, indicating a notable gap between the XAI output and engineering insights for Classes 9 to 16 and 25 to 32. These classes share a common failure mode: pump internal leakage. Unlike the external leakage, internal leakage results in the leaked flow recirculating to the pump inlet rather than exiting the system. This backflow reduces the system's flow rate and the pressure differential across the pump, giving the impression of normal operation at lower pump speeds. Although the selected opaque ML algorithms can detect internal leakage by incorporating pump speed into the feature space (as shown by the grey bars in Figure 15), this failure mode challenges the surrogate model in LIME, reducing interpretability. Figure 15: The interpretability of two black-box algorithms: (**a**) ANN; (**b**) SVM. Based on the engineering insights into pump internal leakages, the pump speed is the most sensitive feature for detecting this failure mode, followed closely by the flow rate delivered to the engine (f3) which significantly decreases during an internal leakage. However, in the XAI explanations for the ANN, pump speed, f3, and p7 were most frequently highlighted as essential features for predicting internal leakages. While engineering insights support the relevance of pump speed and f3, they disagree on the importance of p7, as it is located farther from the pump and experiences less significant pressure changes than p2 or p3. For the SVM, XAI identified flow rate f3 and pressures p5 and p7 as the most sensitive features, but engineering understanding only supports f3. Consequently, XAI explanations for the SVM underperformed relative to the ANN in certain classes (e.g., Classes 9, 10, 11, 13, 29, 31, and 32) associated with internal leakage.