Overview
A predictive maintenance platform for the WLR Swathi weapon-locating radar. The system ingests historical telemetry, maintenance logs, and operational data to forecast subsystem failures before they happen — letting operators shift from reactive repairs to planned, condition-based maintenance. A dashboard surfaces risk scores, time-to-failure estimates, and recommended service windows for each subsystem.
The Problem
Radar subsystems fail in the field with little warning, forcing unplanned downtime during operations. Fixed-interval preventive maintenance either over-services healthy units or misses early failure signatures. A data-driven approach that learns the actual failure signatures from historical data can predict problems days or weeks in advance — turning unplanned failures into scheduled service.
My Role & Contribution
- Built the data ingestion and cleaning pipeline for heterogeneous radar telemetry and maintenance logs
- Trained and validated failure-prediction models against held-out historical incidents
- Designed the operator dashboard and integrated the serving layer with the existing maintenance workflow
Approach
- Clean and align multi-source telemetry, maintenance records, and operational logs onto a common time base
- Engineer features that capture drift, anomaly, and degradation patterns across subsystems
- Train gradient-boosted and time-series models (XGBoost, rolling-window classifiers, survival analysis) to predict failure probability and time-to-failure
- Serve predictions through a FastAPI service consumed by a React dashboard with Plotly visualizations
- Close the loop — capture operator feedback on each prediction to retrain and recalibrate
Tech Stack
Python
scikit-learn
XGBoost
Pandas
Time-series analysis
FastAPI
React.js
Plotly
Results & Impact
- Shifted the maintenance posture from reactive to condition-based for covered subsystems
- Deployed and in active use by maintenance operators
Note: Certain implementation details are covered by DRDO confidentiality. The case study describes the approach at a level appropriate for public sharing.
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