Neha Thomas completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector from vibration data: visualized healthy vs. faulty snapshots, engineered time-domain and envelope-spectrum features, localized the fault to the outer race via BPFO, and trained a compact depth-capped decision tree on training bearings only. Evaluated on held-out bearings with reported catch-rate and false-alarm rate, then justified a 0.38 alarm threshold using explicit cost tradeoffs in a maintenance memo.

• Leakage-Free Feature Engineering
• Physics-Based Fault Localization
• Honest Held-Out Evaluation
• Cost-Based Operational Decision-Making