Sherafgan Khan completed Predictive Maintenance: Industrial ML for Fault Detection

Built a compact rolling-bearing fault detector end to end: engineered RMS, kurtosis, crest factor, and envelope-spectrum defect-frequency features; trained a shallow decision tree on training bearings only; and validated it on a held-out set with an honest confusion-based catch-rate and false-alarm analysis. The final memo defended a 0.40 alarm threshold using the candidate’s own cost numbers, correctly identified an outer-race fault from BPFO, and translated the result into a maintenance decision a non-expert could act on.

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