Hari Mahajan completed Predictive Maintenance: Industrial ML for Fault Detection

Built a compact rolling-bearing fault detector end to end: loaded and interpreted vibration snapshots, selected kurtosis as the strongest impulsive-fault feature, trained a shallow decision tree on training bearings only, and evaluated it on a held-out set with reported catch-rate and false-alarm rate. Also wrote a maintenance memo that defended a 0.35 alarm threshold using explicit cost trade-offs and a plain-language explanation for non-experts.

• Leakage-Free Model Training
• Honest Held-Out Evaluation
• Cost-Based Thresholding