Ahmed Memon completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector using vibration data: engineered RMS, kurtosis, crest factor, and envelope-spectrum defect-band energy; trained a depth-capped decision tree on training bearings only; and evaluated it on held-out healthy/faulty bearings with catch-rate and false-alarm metrics. The final memo defended a cost-based alarm threshold and correctly localized the fault to the outer race from BPFO.

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