Afshan Momin completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector using vibration features, envelope-spectrum energy at the supplied defect frequencies, and a compact decision tree trained only on the training bearings. The candidate also validated the model on a held-out set with a full confusion matrix, selected a cost-based alarm threshold, and correctly localized the fault to the outer race via BPFO dominance.

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