Anurag completed Predictive Maintenance: Industrial ML for Fault Detection

Built a compact rolling-bearing fault detector end to end: engineered time-domain features that separated healthy from impulsive faulty vibration, trained a depth-capped decision tree on training bearings only, and evaluated it on held-out bearings with explicit catch-rate and false-alarm metrics. The final maintenance memo justified a 0.42 alarm threshold using the candidate’s own cost numbers and correctly localized the fault to the outer race via BPFO.

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