Nipun Grover completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector from vibration data: engineered RMS, kurtosis, crest factor, and defect-frequency envelope energy; trained a depth-capped decision tree on training bearings only; and evaluated it on held-out bearings with catch-rate and false-alarm metrics. The final memo justified a 0.60 alarm threshold using the candidate’s own cost numbers and correctly localized the fault to the outer race via BPFO dominance.

• Leakage-Free Feature Engineering
• Honest Model Evaluation
• Cost-Based Decision Making
• Interpreting Physical Fault Signatures