Krrish Chhabra completed Predictive Maintenance: Industrial ML for Fault Detection

Built a compact rolling-bearing fault detector end to end: visualized vibration snapshots, identified impulsive fault behavior with kurtosis, trained a depth-capped decision tree on training bearings only, and evaluated it on a held-out set with catch-rate and false-alarm rate. The candidate also defended an alarm threshold using concrete maintenance costs and localized the fault to the outer race from BPFO.

• Leakage-Free Model Training
• Honest Held-Out Evaluation
• Physics-Based Fault Localization