Luzen completed Predictive Maintenance: Industrial ML for Fault Detection

Built a compact rolling-bearing fault detector that uses vibration features and envelope-spectrum energy to localize the fault to the outer race (BPFO). Trained a shallow decision tree on training bearings only, evaluated it on held-out bearings with catch-rate and false-alarm metrics, and wrote a maintenance memo that justifies the alarm threshold using the candidate’s own results.

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