Parth Malasne 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 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 and justified an alarm threshold with a clear cost-based maintenance memo.

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