@saminsiddiqui

The core ML architecture is done, so my final focus was transforming AeroGlass XAI into a production-ready enterprise tool.

The ‘GO / NO-GO’ Pre-Flight Briefing: I built a new operational dashboard that allows dispatchers to select the specific port and starboard engines mounted to an airframe. The app runs a real-time check against the Attention BiLSTM. If the RUL dips below the 30-cycle threshold, the UI triggers a dynamic HTML/CSS ‘NO-GO’ lockdown, pulling the top 3 thermodynamic stressors from the SHAP array to justify the grounding to the mechanics.

Financial Interactivity (The ROI Sandbox): Predictive maintenance is only useful if it saves money. I upgraded the Operational ROI Calculator with a dynamic slider that simulates the ‘AI False Negative Rate.’ This stress-tests the financial model in real-time. If you push the AI’s error rate too high, the dynamic metrics instantly flip from Cyan (‘Millions Saved’) to Red (‘Millions Lost’)

The finish line is near the corner. It will be ready to ship soon!!!

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.

I’m currently building AeroGlass XAI, a web-based command center for jet engine maintenance. While I have a robust backend foundation using the NASA CMAPSS FD004 dataset, my focus for this hackathon is the “Glass Box” transition. I am taking pre-computed BiLSTM predictions and SHAP arrays and wrapping them into a production-ready Streamlit architecture.

I have just finalized a scalable, multi-file directory structure to ensure this isn’t just a script, but an enterprise-grade application. I’ve also implemented the native dark-mode aerospace aesthetic in the configuration.

My aim is to turn static math into a “Live Telemetry” simulator and an interactive XAI audit tool where users can drill down into thermodynamic sensor impacts in real-time.