Mohammed Imad Uddin Hameed completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector from vibration data: engineered RMS, kurtosis, crest factor, and envelope-spectrum defect-frequency features; trained a compact depth-capped decision tree on training bearings only; and evaluated it on held-out bearings with catch-rate and false-alarm metrics. The candidate also localized the fault to the outer race via BPFO dominance and justified an alarm threshold with explicit maintenance-cost tradeoffs.

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