Naman Rawat completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector from vibration data: visualized the signal, engineered RMS/kurtosis/crest-factor and defect-frequency envelope features, trained a compact decision tree on training bearings only, and validated it on held-out bearings with catch-rate and false-alarm metrics. The final memo justified a 0.40 alarm threshold using explicit maintenance costs and correctly localized the fault to the outer race via BPFO dominance.

• Leakage-Free Feature Engineering
• Honest Held-Out Evaluation
• Physics-Based Fault Localization
• Cost-Based Alarm Decisioning