Govinda Akumal completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector from vibration data: engineered time-domain and envelope-spectrum features, trained a depth-capped decision tree on training bearings only, and evaluated it on a held-out set with explicit catch-rate and false-alarm metrics. The final memo defended a 0.40 alarm threshold using the student’s own cost numbers and correctly localized the fault to the outer race via BPFO.

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