The core engine of AeroGlass XAI is now fully operational. The goal of this project wasn’t just to predict jet engine failure but to force the neural network to explain its reasoning to human engineers.

Over the last couple of hours, I successfully wired the backend prediction arrays into a dynamic, cycle by cycle Explainable AI (XAI) interface using Streamlit and Matplotlib.

The Breakthrough: I caught the BiLSTM doing something fascinating while I was building the visualizer. Near the end of an engine’s life, the model was aggressively penalizing the Remaining Useful Life (RUL) based on Core Speed (S9) but simultaneously extending it based on HPC Static Pressure (S11). Because speed and pressure are physically correlated (Multicollinearity), the AI was mathematically ‘balancing’ its weights.

By wrapping the SHAP values in a color-coded UI (Red for Core Thermodynamic, Blue for Mechanical), the dashboard immediately exposed this AI logic flaw to the human eye. This is exactly why safety critical aerospace requires ‘Glass Box’ interpretability.