Purely data-driven models frequently miss failures because they ignore physical reality. This project bridges that gap by combining machine learning with mechanical domain expertise, delivering a high-fidelity asset pipeline that respects physical variables like thermal limits, stress, and vibrational frequencies.
Data Audit & Signal Processing
Ingest raw data streams, resolve missing or corrupted sensor packets, and perform advanced exploratory data analysis (EDA) to map data health against actual operational history.
Hybrid Feature Engineering & Failure Physics
Calculate physical degradation features (e.g., statistical frequency-domain indicators to translate raw sensor signals into meaningful indicators of mechanical wear.
Machine Learning Modeling
Develop and optimize robust predictive models (leveraging specialized algorithms tailored for highly imbalanced datasets where failures are rare) to maximize early detection windows while minimizing false alarms