Toseef Haider completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector using vibration snapshots, physics-based time and frequency features, and a compact decision tree sized for embedded use. Evaluated on held-out bearings with a full confusion matrix, then wrote a maintenance memo that justified a 0.40 alarm threshold using the candidate’s own catch-rate, false-alarm rate, and cost numbers while correctly localizing the fault to the outer race from BPFO.

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