Muhammad Talha Inam 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 and defect-frequency envelope features, trained a compact depth-capped decision tree on training bearings only, and evaluated it on a held-out set with catch-rate and false-alarm metrics. The memo defended a cost-based alarm threshold using the candidate’s own numbers and correctly localized the fault to the outer race via BPFO.

• Leakage-Free Feature Engineering
• Honest Held-Out Evaluation
• Cost-Based Operational Decision-Making