Priyanka completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector using vibration snapshots, physics-based features, and a compact decision tree trained only on the training bearings. Evaluated it on held-out healthy and faulty bearings, identified the outer-race fault from the dominant BPFO energy, and justified an alarm threshold with concrete catch-rate, false-alarm, and cost numbers in a maintenance memo.

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