Physics-informed predictive maintenance (PdM) framework for rocket engines. Leverages Random Forest Regression on NASA CMAPSS telemetry (FD001) to automate engine safety clearance and solve the "Certification Bottleneck" in orbital launch.
This repository contains our VXP2 Prototype 1.0A for IISc FSID/STEM Technical Assessment. A research-grade implementation of Physics-Informed predictive health monitoring. Developed for IISc, this prototype utilizes Random Forest architectures to translate NASA CMAPSS telemetry into verified engine safety clearance protocols.
This repository contains our VXP2 Iteration 1.0B for IISc FSID/STEM Technical Assessment. Hybrid AI-Physics Predictive Maintenance for Aerospace. Integrating LSTM Neural Networks with Thermodynamic Guardrails to achieve >80% accuracy in engine RUL prediction. Built for Orion Spacetech.
AI and machine learning portfolio presenting projects in deep learning, computer vision, and intelligent systems.
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