Rajat Mishra completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector from vibration data: plotted and interpreted the faulty waveform, engineered RMS/kurtosis/crest and envelope-spectrum defect-band features, trained a compact depth-1 decision tree on training bearings only, and evaluated it on held-out bearings with catch-rate and false-alarm metrics. The final memo defended a 0.41 alarm threshold using the student’s own cost numbers and correctly localized the fault to the outer race via BPFO.

• Leakage-Free Feature Engineering
• Honest Held-Out Evaluation
• Physics-Based Fault Localization
• Compact Interpretable Modeling