Devang Rathi completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector on vibration data: plotted and interpreted a faulty snapshot, used kurtosis to capture impulsive behavior, trained a shallow decision tree on training bearings only, and evaluated it on held-out healthy and faulty bearings. The final memo defended a 0.60 alarm threshold with catch-rate, false-alarm, and cost figures, and identified the outer race as the likely fault location.

• Leakage-Free Feature Engineering
• Honest Held-Out Evaluation
• Compact Interpretable Modeling
• Maintenance-Focused Decision Framing