Ishmam Jahin completed Predictive Maintenance: Industrial ML for Fault Detection

Built a compact rolling-bearing fault detector end to end: engineered time-domain features, used envelope-spectrum energy at supplied defect frequencies, trained a shallow decision tree on training bearings only, and evaluated it on a held-out set with catch-rate and false-alarm rate. The candidate also wrote a maintenance memo that justified a low alarm threshold using explicit cost trade-offs and correctly localized the fault to the outer race from the dominant BPFO frequency.

• Leakage-Free Feature Engineering
• Honest Model Evaluation
• Cost-Based Decision Making
• Plain-Language Technical Explanation