Shubham Ahuja completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector from vibration data: visualized the signal, engineered RMS/kurtosis/crest-factor and envelope-spectrum features at the supplied defect frequencies, localized the fault to the outer race via BPFO, and trained a depth-capped decision tree on training bearings only. Evaluated on a held-out set with catch-rate and false-alarm rate, then justified a 0.50 alarm threshold using explicit miss-vs-inspection costs in a maintenance memo.

• Leakage-Free Feature Engineering
• Honest Held-Out Evaluation
• Physics-Based Fault Localization
• Cost-Based Operational Decisioning