Sumukha H Hegde completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector using vibration snapshots, physics-based time-domain features, and a compact decision tree trained only on the training bearings. Evaluated it on held-out healthy and faulty bearings, reported catch-rate and false-alarm rate, and justified an alarm threshold with concrete maintenance-cost tradeoffs while identifying the fault as an outer-race/BPFO issue.

• Leakage-Free Model Training
• Honest Held-Out Evaluation
• Cost-Based Threshold Selection
• Physics-Informed Fault Interpretation